---
_id: '115'
abstract:
- lang: eng
text: 'Whenever customers have to decide between different instances of the same
product, they are interested in buying the best product. In contrast, companies
are interested in reducing the construction effort (and usually as a consequence
thereof, the quality) to gain profit. The described setting is widely known as
opposed preferences in quality of the product and also applies to the context
of service-oriented computing. In general, service-oriented computing emphasizes
the construction of large software systems out of existing services, where services
are small and self-contained pieces of software that adhere to a specified interface.
Several implementations of the same interface are considered as several instances
of the same service. Thereby, customers are interested in buying the best service
implementation for their service composition wrt. to metrics, such as costs, energy,
memory consumption, or execution time. One way to ensure the service quality is
to employ certificates, which can come in different kinds: Technical certificates
proving correctness can be automatically constructed by the service provider and
again be automatically checked by the user. Digital certificates allow proof of
the integrity of a product. Other certificates might be rolled out if service
providers follow a good software construction principle, which is checked in annual
audits. Whereas all of these certificates are handled differently in service markets,
what they have in common is that they influence the buying decisions of customers.
In this paper, we review state-of-the-art developments in certification with respect
to service-oriented computing. We not only discuss how certificates are constructed
and handled in service-oriented computing but also review the effects of certificates
on the market from an economic perspective.'
author:
- first_name: Marie-Christine
full_name: Jakobs, Marie-Christine
last_name: Jakobs
- first_name: Julia
full_name: Krämer, Julia
last_name: Krämer
- first_name: Dirk
full_name: van Straaten, Dirk
id: '10311'
last_name: van Straaten
- first_name: Theodor
full_name: Lettmann, Theodor
id: '315'
last_name: Lettmann
orcid: 0000-0001-5859-2457
citation:
ama: 'Jakobs M-C, Krämer J, van Straaten D, Lettmann T. Certification Matters for
Service Markets. In: Marcelo De Barros, Janusz Klink,Tadeus Uhl TP, ed. The
Ninth International Conferences on Advanced Service Computing (SERVICE COMPUTATION).
; 2017:7-12.'
apa: Jakobs, M.-C., Krämer, J., van Straaten, D., & Lettmann, T. (2017). Certification
Matters for Service Markets. In T. P. Marcelo De Barros, Janusz Klink,Tadeus Uhl
(Ed.), The Ninth International Conferences on Advanced Service Computing (SERVICE
COMPUTATION) (pp. 7–12).
bibtex: '@inproceedings{Jakobs_Krämer_van Straaten_Lettmann_2017, title={Certification
Matters for Service Markets}, booktitle={The Ninth International Conferences on
Advanced Service Computing (SERVICE COMPUTATION)}, author={Jakobs, Marie-Christine
and Krämer, Julia and van Straaten, Dirk and Lettmann, Theodor}, editor={Marcelo
De Barros, Janusz Klink,Tadeus Uhl, Thomas PrinzEditor}, year={2017}, pages={7–12}
}'
chicago: Jakobs, Marie-Christine, Julia Krämer, Dirk van Straaten, and Theodor Lettmann.
“Certification Matters for Service Markets.” In The Ninth International Conferences
on Advanced Service Computing (SERVICE COMPUTATION), edited by Thomas Prinz
Marcelo De Barros, Janusz Klink,Tadeus Uhl, 7–12, 2017.
ieee: M.-C. Jakobs, J. Krämer, D. van Straaten, and T. Lettmann, “Certification Matters
for Service Markets,” in The Ninth International Conferences on Advanced Service
Computing (SERVICE COMPUTATION), 2017, pp. 7–12.
mla: Jakobs, Marie-Christine, et al. “Certification Matters for Service Markets.”
The Ninth International Conferences on Advanced Service Computing (SERVICE
COMPUTATION), edited by Thomas Prinz Marcelo De Barros, Janusz Klink,Tadeus
Uhl, 2017, pp. 7–12.
short: 'M.-C. Jakobs, J. Krämer, D. van Straaten, T. Lettmann, in: T.P. Marcelo
De Barros, Janusz Klink,Tadeus Uhl (Ed.), The Ninth International Conferences
on Advanced Service Computing (SERVICE COMPUTATION), 2017, pp. 7–12.'
date_created: 2017-10-17T12:41:14Z
date_updated: 2022-01-06T06:51:02Z
ddc:
- '040'
department:
- _id: '77'
- _id: '355'
- _id: '179'
editor:
- first_name: Thomas Prinz
full_name: Marcelo De Barros, Janusz Klink,Tadeus Uhl, Thomas Prinz
last_name: Marcelo De Barros, Janusz Klink,Tadeus Uhl
file:
- access_level: closed
content_type: application/pdf
creator: florida
date_created: 2018-03-21T13:04:12Z
date_updated: 2018-03-21T13:04:12Z
file_id: '1564'
file_name: 115-JakobsKraemerVanStraatenLettmann2017.pdf
file_size: 133531
relation: main_file
success: 1
file_date_updated: 2018-03-21T13:04:12Z
has_accepted_license: '1'
language:
- iso: eng
page: 7-12
project:
- _id: '1'
name: SFB 901
- _id: '10'
name: SFB 901 - Subprojekt B2
- _id: '11'
name: SFB 901 - Subproject B3
- _id: '12'
name: SFB 901 - Subproject B4
- _id: '8'
name: SFB 901 - Subproject A4
- _id: '2'
name: SFB 901 - Project Area A
- _id: '3'
name: SFB 901 - Project Area B
publication: The Ninth International Conferences on Advanced Service Computing (SERVICE
COMPUTATION)
status: public
title: Certification Matters for Service Markets
type: conference
user_id: '477'
year: '2017'
...
---
_id: '1158'
abstract:
- lang: eng
text: In this paper, we present the annotation challenges we have encountered when
working on a historical language that was undergoing elaboration processes. We
especially focus on syntactic ambiguity and gradience in Middle Low German, which
causes uncertainty to some extent. Since current annotation tools consider construction
contexts and the dynamics of the grammaticalization only partially, we plan to
extend CorA – a web-based annotation tool for historical and other non-standard
language data – to capture elaboration phenomena and annotator unsureness. Moreover,
we seek to interactively learn morphological as well as syntactic annotations.
author:
- first_name: Nina
full_name: Seemann, Nina
id: '65408'
last_name: Seemann
- first_name: Marie-Luis
full_name: Merten, Marie-Luis
last_name: Merten
- first_name: Michaela
full_name: Geierhos, Michaela
id: '42496'
last_name: Geierhos
orcid: 0000-0002-8180-5606
- first_name: Doris
full_name: Tophinke, Doris
last_name: Tophinke
- first_name: Eyke
full_name: Hüllermeier, Eyke
last_name: Hüllermeier
citation:
ama: 'Seemann N, Merten M-L, Geierhos M, Tophinke D, Hüllermeier E. Annotation Challenges
for Reconstructing the Structural Elaboration of Middle Low German. In: Proceedings
of the Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage,
Social Sciences, Humanities and Literature. Stroudsburg, PA, USA: Association
for Computational Linguistics (ACL); 2017:40-45. doi:10.18653/v1/W17-2206'
apa: 'Seemann, N., Merten, M.-L., Geierhos, M., Tophinke, D., & Hüllermeier,
E. (2017). Annotation Challenges for Reconstructing the Structural Elaboration
of Middle Low German. In Proceedings of the Joint SIGHUM Workshop on Computational
Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature
(pp. 40–45). Stroudsburg, PA, USA: Association for Computational Linguistics (ACL).
https://doi.org/10.18653/v1/W17-2206'
bibtex: '@inproceedings{Seemann_Merten_Geierhos_Tophinke_Hüllermeier_2017, place={Stroudsburg,
PA, USA}, title={Annotation Challenges for Reconstructing the Structural Elaboration
of Middle Low German}, DOI={10.18653/v1/W17-2206},
booktitle={Proceedings of the Joint SIGHUM Workshop on Computational Linguistics
for Cultural Heritage, Social Sciences, Humanities and Literature}, publisher={Association
for Computational Linguistics (ACL)}, author={Seemann, Nina and Merten, Marie-Luis
and Geierhos, Michaela and Tophinke, Doris and Hüllermeier, Eyke}, year={2017},
pages={40–45} }'
chicago: 'Seemann, Nina, Marie-Luis Merten, Michaela Geierhos, Doris Tophinke, and
Eyke Hüllermeier. “Annotation Challenges for Reconstructing the Structural Elaboration
of Middle Low German.” In Proceedings of the Joint SIGHUM Workshop on Computational
Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature,
40–45. Stroudsburg, PA, USA: Association for Computational Linguistics (ACL),
2017. https://doi.org/10.18653/v1/W17-2206.'
ieee: N. Seemann, M.-L. Merten, M. Geierhos, D. Tophinke, and E. Hüllermeier, “Annotation
Challenges for Reconstructing the Structural Elaboration of Middle Low German,”
in Proceedings of the Joint SIGHUM Workshop on Computational Linguistics for
Cultural Heritage, Social Sciences, Humanities and Literature, Vancouver,
BC, Canada, 2017, pp. 40–45.
mla: Seemann, Nina, et al. “Annotation Challenges for Reconstructing the Structural
Elaboration of Middle Low German.” Proceedings of the Joint SIGHUM Workshop
on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities
and Literature, Association for Computational Linguistics (ACL), 2017, pp.
40–45, doi:10.18653/v1/W17-2206.
short: 'N. Seemann, M.-L. Merten, M. Geierhos, D. Tophinke, E. Hüllermeier, in:
Proceedings of the Joint SIGHUM Workshop on Computational Linguistics for Cultural
Heritage, Social Sciences, Humanities and Literature, Association for Computational
Linguistics (ACL), Stroudsburg, PA, USA, 2017, pp. 40–45.'
conference:
end_date: 2017-08-04
location: Vancouver, BC, Canada
name: Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage,
Social Sciences, Humanities and Literature (LaTeCH-CLfL 2017)
start_date: 2017-07-31
date_created: 2018-01-31T15:32:33Z
date_updated: 2022-01-06T06:51:03Z
department:
- _id: '36'
- _id: '579'
- _id: '115'
- _id: '355'
- _id: '615'
doi: 10.18653/v1/W17-2206
language:
- iso: eng
page: 40-45
place: Stroudsburg, PA, USA
project:
- _id: '39'
name: InterGramm
publication: Proceedings of the Joint SIGHUM Workshop on Computational Linguistics
for Cultural Heritage, Social Sciences, Humanities and Literature
publication_status: published
publisher: Association for Computational Linguistics (ACL)
quality_controlled: '1'
status: public
title: Annotation Challenges for Reconstructing the Structural Elaboration of Middle
Low German
type: conference
user_id: '13929'
year: '2017'
...
---
_id: '5694'
author:
- first_name: Nino Noel
full_name: Schnitker, Nino Noel
last_name: Schnitker
citation:
ama: Schnitker NN. Genetischer Algorithmus zur Erstellung von Ensembles von Nested
Dichotomies. Universität Paderborn; 2017.
apa: Schnitker, N. N. (2017). Genetischer Algorithmus zur Erstellung von Ensembles
von Nested Dichotomies. Universität Paderborn.
bibtex: '@book{Schnitker_2017, title={Genetischer Algorithmus zur Erstellung von
Ensembles von Nested Dichotomies}, publisher={Universität Paderborn}, author={Schnitker,
Nino Noel}, year={2017} }'
chicago: Schnitker, Nino Noel. Genetischer Algorithmus zur Erstellung von Ensembles
von Nested Dichotomies. Universität Paderborn, 2017.
ieee: N. N. Schnitker, Genetischer Algorithmus zur Erstellung von Ensembles von
Nested Dichotomies. Universität Paderborn, 2017.
mla: Schnitker, Nino Noel. Genetischer Algorithmus zur Erstellung von Ensembles
von Nested Dichotomies. Universität Paderborn, 2017.
short: N.N. Schnitker, Genetischer Algorithmus zur Erstellung von Ensembles von
Nested Dichotomies, Universität Paderborn, 2017.
date_created: 2018-11-15T08:10:48Z
date_updated: 2022-01-06T07:02:35Z
department:
- _id: '355'
language:
- iso: ger
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
publisher: Universität Paderborn
status: public
supervisor:
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
title: Genetischer Algorithmus zur Erstellung von Ensembles von Nested Dichotomies
type: bachelorsthesis
user_id: '477'
year: '2017'
...
---
_id: '5722'
author:
- first_name: Pritha
full_name: Gupta, Pritha
last_name: Gupta
- first_name: Alexander
full_name: Hetzer, Alexander
id: '38209'
last_name: Hetzer
- first_name: Tanja
full_name: Tornede, Tanja
last_name: Tornede
- first_name: Sebastian
full_name: Gottschalk, Sebastian
last_name: Gottschalk
- first_name: Andreas
full_name: Kornelsen, Andreas
last_name: Kornelsen
- first_name: Sebastian
full_name: Osterbrink, Sebastian
last_name: Osterbrink
- first_name: Karlson
full_name: Pfannschmidt, Karlson
last_name: Pfannschmidt
- first_name: Eyke
full_name: Hüllermeier, Eyke
last_name: Hüllermeier
citation:
ama: 'Gupta P, Hetzer A, Tornede T, et al. jPL: A Java-based Software Framework
for Preference Learning. In: ; 2017.'
apa: 'Gupta, P., Hetzer, A., Tornede, T., Gottschalk, S., Kornelsen, A., Osterbrink,
S., … Hüllermeier, E. (2017). jPL: A Java-based Software Framework for Preference
Learning. Presented at the WDA 2017 Workshops: KDML, FGWM, IR, and FGDB, Rostock.'
bibtex: '@inproceedings{Gupta_Hetzer_Tornede_Gottschalk_Kornelsen_Osterbrink_Pfannschmidt_Hüllermeier_2017,
title={jPL: A Java-based Software Framework for Preference Learning}, author={Gupta,
Pritha and Hetzer, Alexander and Tornede, Tanja and Gottschalk, Sebastian and
Kornelsen, Andreas and Osterbrink, Sebastian and Pfannschmidt, Karlson and Hüllermeier,
Eyke}, year={2017} }'
chicago: 'Gupta, Pritha, Alexander Hetzer, Tanja Tornede, Sebastian Gottschalk,
Andreas Kornelsen, Sebastian Osterbrink, Karlson Pfannschmidt, and Eyke Hüllermeier.
“JPL: A Java-Based Software Framework for Preference Learning,” 2017.'
ieee: 'P. Gupta et al., “jPL: A Java-based Software Framework for Preference
Learning,” presented at the WDA 2017 Workshops: KDML, FGWM, IR, and FGDB, Rostock,
2017.'
mla: 'Gupta, Pritha, et al. JPL: A Java-Based Software Framework for Preference
Learning. 2017.'
short: 'P. Gupta, A. Hetzer, T. Tornede, S. Gottschalk, A. Kornelsen, S. Osterbrink,
K. Pfannschmidt, E. Hüllermeier, in: 2017.'
conference:
end_date: 13.09.2017
location: Rostock
name: 'WDA 2017 Workshops: KDML, FGWM, IR, and FGDB'
start_date: 11.09.2017
date_created: 2018-11-19T07:32:31Z
date_updated: 2022-01-06T07:02:37Z
department:
- _id: '355'
extern: '1'
language:
- iso: eng
status: public
title: 'jPL: A Java-based Software Framework for Preference Learning'
type: conference_abstract
user_id: '38209'
year: '2017'
...
---
_id: '5724'
author:
- first_name: Alexander
full_name: Hetzer, Alexander
id: '38209'
last_name: Hetzer
- first_name: Tanja
full_name: Tornede, Tanja
last_name: Tornede
citation:
ama: Hetzer A, Tornede T. Solving the Container Pre-Marshalling Problem Using
Reinforcement Learning and Structured Output Prediction. Universität Paderborn;
2017.
apa: Hetzer, A., & Tornede, T. (2017). Solving the Container Pre-Marshalling
Problem using Reinforcement Learning and Structured Output Prediction. Universität
Paderborn.
bibtex: '@book{Hetzer_Tornede_2017, title={Solving the Container Pre-Marshalling
Problem using Reinforcement Learning and Structured Output Prediction}, publisher={Universität
Paderborn}, author={Hetzer, Alexander and Tornede, Tanja}, year={2017} }'
chicago: Hetzer, Alexander, and Tanja Tornede. Solving the Container Pre-Marshalling
Problem Using Reinforcement Learning and Structured Output Prediction. Universität
Paderborn, 2017.
ieee: A. Hetzer and T. Tornede, Solving the Container Pre-Marshalling Problem
using Reinforcement Learning and Structured Output Prediction. Universität
Paderborn, 2017.
mla: Hetzer, Alexander, and Tanja Tornede. Solving the Container Pre-Marshalling
Problem Using Reinforcement Learning and Structured Output Prediction. Universität
Paderborn, 2017.
short: A. Hetzer, T. Tornede, Solving the Container Pre-Marshalling Problem Using
Reinforcement Learning and Structured Output Prediction, Universität Paderborn,
2017.
date_created: 2018-11-19T07:49:13Z
date_updated: 2022-01-06T07:02:37Z
department:
- _id: '355'
- _id: '199'
language:
- iso: eng
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
publisher: Universität Paderborn
status: public
supervisor:
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Kevin
full_name: Tierney, Kevin
last_name: Tierney
title: Solving the Container Pre-Marshalling Problem using Reinforcement Learning
and Structured Output Prediction
type: mastersthesis
user_id: '477'
year: '2017'
...
---
_id: '71'
abstract:
- lang: eng
text: Today, software verification tools have reached the maturity to be used for
large scale programs. Different tools perform differently well on varying code.
A software developer is hence faced with the problem of choosing a tool appropriate
for her program at hand. A ranking of tools on programs could facilitate the choice.
Such rankings can, however, so far only be obtained by running all considered
tools on the program.In this paper, we present a machine learning approach to
predicting rankings of tools on programs. The method builds upon so-called label
ranking algorithms, which we complement with appropriate kernels providing a similarity
measure for programs. Our kernels employ a graph representation for software source
code that mixes elements of control flow and program dependence graphs with abstract
syntax trees. Using data sets from the software verification competition SV-COMP,
we demonstrate our rank prediction technique to generalize well and achieve a
rather high predictive accuracy (rank correlation > 0.6).
author:
- first_name: Mike
full_name: Czech, Mike
last_name: Czech
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Marie-Christine
full_name: Jakobs, Marie-Christine
last_name: Jakobs
- first_name: Heike
full_name: Wehrheim, Heike
id: '573'
last_name: Wehrheim
citation:
ama: 'Czech M, Hüllermeier E, Jakobs M-C, Wehrheim H. Predicting Rankings of Software
Verification Tools. In: Proceedings of the 3rd International Workshop on Software
Analytics. SWAN’17. ; 2017:23-26. doi:10.1145/3121257.3121262'
apa: Czech, M., Hüllermeier, E., Jakobs, M.-C., & Wehrheim, H. (2017). Predicting
Rankings of Software Verification Tools. In Proceedings of the 3rd International
Workshop on Software Analytics (pp. 23–26). https://doi.org/10.1145/3121257.3121262
bibtex: '@inproceedings{Czech_Hüllermeier_Jakobs_Wehrheim_2017, series={SWAN’17},
title={Predicting Rankings of Software Verification Tools}, DOI={10.1145/3121257.3121262},
booktitle={Proceedings of the 3rd International Workshop on Software Analytics},
author={Czech, Mike and Hüllermeier, Eyke and Jakobs, Marie-Christine and Wehrheim,
Heike}, year={2017}, pages={23–26}, collection={SWAN’17} }'
chicago: Czech, Mike, Eyke Hüllermeier, Marie-Christine Jakobs, and Heike Wehrheim.
“Predicting Rankings of Software Verification Tools.” In Proceedings of the
3rd International Workshop on Software Analytics, 23–26. SWAN’17, 2017. https://doi.org/10.1145/3121257.3121262.
ieee: M. Czech, E. Hüllermeier, M.-C. Jakobs, and H. Wehrheim, “Predicting Rankings
of Software Verification Tools,” in Proceedings of the 3rd International Workshop
on Software Analytics, 2017, pp. 23–26.
mla: Czech, Mike, et al. “Predicting Rankings of Software Verification Tools.” Proceedings
of the 3rd International Workshop on Software Analytics, 2017, pp. 23–26,
doi:10.1145/3121257.3121262.
short: 'M. Czech, E. Hüllermeier, M.-C. Jakobs, H. Wehrheim, in: Proceedings of
the 3rd International Workshop on Software Analytics, 2017, pp. 23–26.'
date_created: 2017-10-17T12:41:05Z
date_updated: 2022-01-06T07:03:28Z
ddc:
- '000'
department:
- _id: '355'
- _id: '77'
doi: 10.1145/3121257.3121262
file:
- access_level: closed
content_type: application/pdf
creator: ups
date_created: 2018-11-02T14:24:29Z
date_updated: 2018-11-02T14:24:29Z
file_id: '5271'
file_name: fsews17swan-swanmain1.pdf
file_size: 822383
relation: main_file
success: 1
file_date_updated: 2018-11-02T14:24:29Z
has_accepted_license: '1'
language:
- iso: eng
page: 23-26
project:
- _id: '1'
name: SFB 901
- _id: '12'
name: SFB 901 - Subprojekt B4
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '3'
name: SFB 901 - Project Area B
- _id: '11'
name: SFB 901 - Subproject B3
publication: Proceedings of the 3rd International Workshop on Software Analytics
series_title: SWAN'17
status: public
title: Predicting Rankings of Software Verification Tools
type: conference
user_id: '15504'
year: '2017'
...
---
_id: '72'
abstract:
- lang: eng
text: 'Software verification competitions, such as the annual SV-COMP, evaluate
software verification tools with respect to their effectivity and efficiency.
Typically, the outcome of a competition is a (possibly category-specific) ranking
of the tools. For many applications, such as building portfolio solvers, it would
be desirable to have an idea of the (relative) performance of verification tools
on a given verification task beforehand, i.e., prior to actually running all tools
on the task.In this paper, we present a machine learning approach to predicting
rankings of tools on verification tasks. The method builds upon so-called label
ranking algorithms, which we complement with appropriate kernels providing a similarity
measure for verification tasks. Our kernels employ a graph representation for
software source code that mixes elements of control flow and program dependence
graphs with abstract syntax trees. Using data sets from SV-COMP, we demonstrate
our rank prediction technique to generalize well and achieve a rather high predictive
accuracy. In particular, our method outperforms a recently proposed feature-based
approach of Demyanova et al. (when applied to rank predictions). '
author:
- first_name: Mike
full_name: Czech, Mike
last_name: Czech
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Marie-Christine
full_name: Jakobs, Marie-Christine
last_name: Jakobs
- first_name: Heike
full_name: Wehrheim, Heike
id: '573'
last_name: Wehrheim
citation:
ama: Czech M, Hüllermeier E, Jakobs M-C, Wehrheim H. Predicting Rankings of Software
Verification Competitions.; 2017.
apa: Czech, M., Hüllermeier, E., Jakobs, M.-C., & Wehrheim, H. (2017). Predicting
Rankings of Software Verification Competitions.
bibtex: '@book{Czech_Hüllermeier_Jakobs_Wehrheim_2017, title={Predicting Rankings
of Software Verification Competitions}, author={Czech, Mike and Hüllermeier, Eyke
and Jakobs, Marie-Christine and Wehrheim, Heike}, year={2017} }'
chicago: Czech, Mike, Eyke Hüllermeier, Marie-Christine Jakobs, and Heike Wehrheim.
Predicting Rankings of Software Verification Competitions, 2017.
ieee: M. Czech, E. Hüllermeier, M.-C. Jakobs, and H. Wehrheim, Predicting Rankings
of Software Verification Competitions. 2017.
mla: Czech, Mike, et al. Predicting Rankings of Software Verification Competitions.
2017.
short: M. Czech, E. Hüllermeier, M.-C. Jakobs, H. Wehrheim, Predicting Rankings
of Software Verification Competitions, 2017.
date_created: 2017-10-17T12:41:05Z
date_updated: 2022-01-06T07:03:29Z
ddc:
- '000'
department:
- _id: '77'
- _id: '355'
file:
- access_level: closed
content_type: application/pdf
creator: florida
date_created: 2018-11-21T10:50:11Z
date_updated: 2018-11-21T10:50:11Z
file_id: '5782'
file_name: "Predicting Rankings of So\x81ware Verification Competitions.pdf"
file_size: 869984
relation: main_file
success: 1
file_date_updated: 2018-11-21T10:50:11Z
has_accepted_license: '1'
language:
- iso: eng
project:
- _id: '1'
name: SFB 901
- _id: '11'
name: SFB 901 - Subprojekt B3
- _id: '12'
name: SFB 901 - Subprojekt B4
- _id: '3'
name: SFB 901 - Project Area B
status: public
title: Predicting Rankings of Software Verification Competitions
type: report
user_id: '15504'
year: '2017'
...
---
_id: '10589'
author:
- first_name: J.
full_name: Fürnkranz, J.
last_name: Fürnkranz
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Fürnkranz J, Hüllermeier E. Preference Learning. In: Encyclopedia of Machine
Learning and Data Mining. ; 2017:1000-1005.'
apa: Fürnkranz, J., & Hüllermeier, E. (2017). Preference Learning. In Encyclopedia
of Machine Learning and Data Mining (pp. 1000–1005).
bibtex: '@inbook{Fürnkranz_Hüllermeier_2017, title={Preference Learning}, booktitle={Encyclopedia
of Machine Learning and Data Mining}, author={Fürnkranz, J. and Hüllermeier, Eyke},
year={2017}, pages={1000–1005} }'
chicago: Fürnkranz, J., and Eyke Hüllermeier. “Preference Learning.” In Encyclopedia
of Machine Learning and Data Mining, 1000–1005, 2017.
ieee: J. Fürnkranz and E. Hüllermeier, “Preference Learning,” in Encyclopedia
of Machine Learning and Data Mining, 2017, pp. 1000–1005.
mla: Fürnkranz, J., and Eyke Hüllermeier. “Preference Learning.” Encyclopedia
of Machine Learning and Data Mining, 2017, pp. 1000–05.
short: 'J. Fürnkranz, E. Hüllermeier, in: Encyclopedia of Machine Learning and Data
Mining, 2017, pp. 1000–1005.'
date_created: 2019-07-09T15:37:09Z
date_updated: 2022-01-06T06:50:45Z
department:
- _id: '34'
- _id: '7'
- _id: '355'
language:
- iso: eng
page: 1000-1005
publication: Encyclopedia of Machine Learning and Data Mining
status: public
title: Preference Learning
type: encyclopedia_article
user_id: '49109'
year: '2017'
...
---
_id: '10784'
author:
- first_name: J.
full_name: Fürnkranz, J.
last_name: Fürnkranz
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Fürnkranz J, Hüllermeier E. Preference Learning. In: Sammut C, Webb GI, eds.
Encyclopedia of Machine Learning and Data Mining. Vol 107. Springer; 2017:1000-1005.'
apa: Fürnkranz, J., & Hüllermeier, E. (2017). Preference Learning. In C. Sammut
& G. I. Webb (Eds.), Encyclopedia of Machine Learning and Data Mining
(Vol. 107, pp. 1000–1005). Springer.
bibtex: '@inbook{Fürnkranz_Hüllermeier_2017, title={Preference Learning}, volume={107},
booktitle={Encyclopedia of Machine Learning and Data Mining}, publisher={Springer},
author={Fürnkranz, J. and Hüllermeier, Eyke}, editor={Sammut, C. and Webb, G.I.Editors},
year={2017}, pages={1000–1005} }'
chicago: Fürnkranz, J., and Eyke Hüllermeier. “Preference Learning.” In Encyclopedia
of Machine Learning and Data Mining, edited by C. Sammut and G.I. Webb, 107:1000–1005.
Springer, 2017.
ieee: J. Fürnkranz and E. Hüllermeier, “Preference Learning,” in Encyclopedia
of Machine Learning and Data Mining, vol. 107, C. Sammut and G. I. Webb, Eds.
Springer, 2017, pp. 1000–1005.
mla: Fürnkranz, J., and Eyke Hüllermeier. “Preference Learning.” Encyclopedia
of Machine Learning and Data Mining, edited by C. Sammut and G.I. Webb, vol.
107, Springer, 2017, pp. 1000–05.
short: 'J. Fürnkranz, E. Hüllermeier, in: C. Sammut, G.I. Webb (Eds.), Encyclopedia
of Machine Learning and Data Mining, Springer, 2017, pp. 1000–1005.'
date_created: 2019-07-10T15:44:32Z
date_updated: 2022-01-06T06:50:50Z
department:
- _id: '34'
- _id: '7'
- _id: '355'
editor:
- first_name: C.
full_name: Sammut, C.
last_name: Sammut
- first_name: G.I.
full_name: Webb, G.I.
last_name: Webb
intvolume: ' 107'
language:
- iso: eng
page: 1000-1005
publication: Encyclopedia of Machine Learning and Data Mining
publisher: Springer
status: public
title: Preference Learning
type: book_chapter
user_id: '49109'
volume: 107
year: '2017'
...
---
_id: '1180'
abstract:
- lang: eng
text: These days, there is a strong rise in the needs for machine learning applications,
requiring an automation of machine learning engineering which is referred to as
AutoML. In AutoML the selection, composition and parametrization of machine learning
algorithms is automated and tailored to a specific problem, resulting in a machine
learning pipeline. Current approaches reduce the AutoML problem to optimization
of hyperparameters. Based on recursive task networks, in this paper we present
one approach from the field of automated planning and one evolutionary optimization
approach. Instead of simply parametrizing a given pipeline, this allows for structure
optimization of machine learning pipelines, as well. We evaluate the two approaches
in an extensive evaluation, finding both approaches to have their strengths in
different areas. Moreover, the two approaches outperform the state-of-the-art
tool Auto-WEKA in many settings.
author:
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Wever MD, Mohr F, Hüllermeier E. Automatic Machine Learning: Hierachical Planning
Versus Evolutionary Optimization. In: 27th Workshop Computational Intelligence.
Dortmund; 2017.'
apa: 'Wever, M. D., Mohr, F., & Hüllermeier, E. (2017). Automatic Machine Learning:
Hierachical Planning Versus Evolutionary Optimization. In 27th Workshop Computational
Intelligence. Dortmund.'
bibtex: '@inproceedings{Wever_Mohr_Hüllermeier_2017, place={Dortmund}, title={Automatic
Machine Learning: Hierachical Planning Versus Evolutionary Optimization}, booktitle={27th
Workshop Computational Intelligence}, author={Wever, Marcel Dominik and Mohr,
Felix and Hüllermeier, Eyke}, year={2017} }'
chicago: 'Wever, Marcel Dominik, Felix Mohr, and Eyke Hüllermeier. “Automatic Machine
Learning: Hierachical Planning Versus Evolutionary Optimization.” In 27th Workshop
Computational Intelligence. Dortmund, 2017.'
ieee: 'M. D. Wever, F. Mohr, and E. Hüllermeier, “Automatic Machine Learning: Hierachical
Planning Versus Evolutionary Optimization,” in 27th Workshop Computational
Intelligence, Dortmund, 2017.'
mla: 'Wever, Marcel Dominik, et al. “Automatic Machine Learning: Hierachical Planning
Versus Evolutionary Optimization.” 27th Workshop Computational Intelligence,
2017.'
short: 'M.D. Wever, F. Mohr, E. Hüllermeier, in: 27th Workshop Computational Intelligence,
Dortmund, 2017.'
conference:
end_date: 2017-11-24
location: Dortmund
name: 27th Workshop Computational Intelligence
start_date: 2017-11-23
date_created: 2018-02-22T07:19:18Z
date_updated: 2022-01-06T06:51:09Z
ddc:
- '000'
department:
- _id: '355'
file:
- access_level: closed
content_type: application/pdf
creator: wever
date_created: 2018-11-06T15:28:09Z
date_updated: 2018-11-06T15:28:09Z
file_id: '5387'
file_name: CI Workshop AutoML.pdf
file_size: 323589
relation: main_file
success: 1
file_date_updated: 2018-11-06T15:28:09Z
has_accepted_license: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://publikationen.bibliothek.kit.edu/1000074341/4643874
oa: '1'
place: Dortmund
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
publication: 27th Workshop Computational Intelligence
publication_status: published
status: public
title: 'Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization'
type: conference
user_id: '49109'
year: '2017'
...
---
_id: '15397'
author:
- first_name: Vitaly
full_name: Melnikov, Vitaly
last_name: Melnikov
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Melnikov V, Hüllermeier E. Optimizing the structure of nested dichotomies.
A comparison of two heuristics. In: Hoffmann F, Hüllermeier E, Mikut R, eds. In
Proceedings 27th Workshop Computational Intelligence, Dortmund Germany. KIT
Scientific Publishing; 2017:1-12.'
apa: Melnikov, V., & Hüllermeier, E. (2017). Optimizing the structure of nested
dichotomies. A comparison of two heuristics. In F. Hoffmann, E. Hüllermeier, &
R. Mikut (Eds.), in Proceedings 27th Workshop Computational Intelligence, Dortmund
Germany (pp. 1–12). KIT Scientific Publishing.
bibtex: '@inproceedings{Melnikov_Hüllermeier_2017, title={Optimizing the structure
of nested dichotomies. A comparison of two heuristics}, booktitle={in Proceedings
27th Workshop Computational Intelligence, Dortmund Germany}, publisher={KIT Scientific
Publishing}, author={Melnikov, Vitaly and Hüllermeier, Eyke}, editor={Hoffmann,
F. and Hüllermeier, Eyke and Mikut, R.Editors}, year={2017}, pages={1–12} }'
chicago: Melnikov, Vitaly, and Eyke Hüllermeier. “Optimizing the Structure of Nested
Dichotomies. A Comparison of Two Heuristics.” In In Proceedings 27th Workshop
Computational Intelligence, Dortmund Germany, edited by F. Hoffmann, Eyke
Hüllermeier, and R. Mikut, 1–12. KIT Scientific Publishing, 2017.
ieee: V. Melnikov and E. Hüllermeier, “Optimizing the structure of nested dichotomies.
A comparison of two heuristics,” in in Proceedings 27th Workshop Computational
Intelligence, Dortmund Germany, 2017, pp. 1–12.
mla: Melnikov, Vitaly, and Eyke Hüllermeier. “Optimizing the Structure of Nested
Dichotomies. A Comparison of Two Heuristics.” In Proceedings 27th Workshop
Computational Intelligence, Dortmund Germany, edited by F. Hoffmann et al.,
KIT Scientific Publishing, 2017, pp. 1–12.
short: 'V. Melnikov, E. Hüllermeier, in: F. Hoffmann, E. Hüllermeier, R. Mikut (Eds.),
In Proceedings 27th Workshop Computational Intelligence, Dortmund Germany, KIT
Scientific Publishing, 2017, pp. 1–12.'
date_created: 2019-12-19T15:48:38Z
date_updated: 2022-01-06T06:52:22Z
department:
- _id: '34'
- _id: '7'
- _id: '355'
editor:
- first_name: F.
full_name: Hoffmann, F.
last_name: Hoffmann
- first_name: Eyke
full_name: Hüllermeier, Eyke
last_name: Hüllermeier
- first_name: R.
full_name: Mikut, R.
last_name: Mikut
language:
- iso: eng
page: 1-12
publication: in Proceedings 27th Workshop Computational Intelligence, Dortmund Germany
publisher: KIT Scientific Publishing
status: public
title: Optimizing the structure of nested dichotomies. A comparison of two heuristics
type: conference
user_id: '49109'
year: '2017'
...
---
_id: '15399'
author:
- first_name: M.
full_name: Czech, M.
last_name: Czech
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: M.C.
full_name: Jacobs, M.C.
last_name: Jacobs
- first_name: Heike
full_name: Wehrheim, Heike
last_name: Wehrheim
citation:
ama: 'Czech M, Hüllermeier E, Jacobs MC, Wehrheim H. Predicting rankings of software
verification tools. In: In Proceedings ESEC/FSE Workshops 2017 - 3rd ACM SIGSOFT,
International Workshop on Software Analytics (SWAN 2017), Paderborn Germany.
; 2017.'
apa: Czech, M., Hüllermeier, E., Jacobs, M. C., & Wehrheim, H. (2017). Predicting
rankings of software verification tools. In in Proceedings ESEC/FSE Workshops
2017 - 3rd ACM SIGSOFT, International Workshop on Software Analytics (SWAN 2017),
Paderborn Germany.
bibtex: '@inproceedings{Czech_Hüllermeier_Jacobs_Wehrheim_2017, title={Predicting
rankings of software verification tools}, booktitle={in Proceedings ESEC/FSE Workshops
2017 - 3rd ACM SIGSOFT, International Workshop on Software Analytics (SWAN 2017),
Paderborn Germany}, author={Czech, M. and Hüllermeier, Eyke and Jacobs, M.C. and
Wehrheim, Heike}, year={2017} }'
chicago: Czech, M., Eyke Hüllermeier, M.C. Jacobs, and Heike Wehrheim. “Predicting
Rankings of Software Verification Tools.” In In Proceedings ESEC/FSE Workshops
2017 - 3rd ACM SIGSOFT, International Workshop on Software Analytics (SWAN 2017),
Paderborn Germany, 2017.
ieee: M. Czech, E. Hüllermeier, M. C. Jacobs, and H. Wehrheim, “Predicting rankings
of software verification tools,” in in Proceedings ESEC/FSE Workshops 2017
- 3rd ACM SIGSOFT, International Workshop on Software Analytics (SWAN 2017), Paderborn
Germany, 2017.
mla: Czech, M., et al. “Predicting Rankings of Software Verification Tools.” In
Proceedings ESEC/FSE Workshops 2017 - 3rd ACM SIGSOFT, International Workshop
on Software Analytics (SWAN 2017), Paderborn Germany, 2017.
short: 'M. Czech, E. Hüllermeier, M.C. Jacobs, H. Wehrheim, in: In Proceedings ESEC/FSE
Workshops 2017 - 3rd ACM SIGSOFT, International Workshop on Software Analytics
(SWAN 2017), Paderborn Germany, 2017.'
date_created: 2019-12-19T15:59:42Z
date_updated: 2022-01-06T06:52:22Z
department:
- _id: '34'
- _id: '7'
- _id: '355'
language:
- iso: eng
publication: in Proceedings ESEC/FSE Workshops 2017 - 3rd ACM SIGSOFT, International
Workshop on Software Analytics (SWAN 2017), Paderborn Germany
status: public
title: Predicting rankings of software verification tools
type: conference
user_id: '49109'
year: '2017'
...
---
_id: '15110'
author:
- first_name: Ines
full_name: Couso, Ines
last_name: Couso
- first_name: D.
full_name: Dubois, D.
last_name: Dubois
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Couso I, Dubois D, Hüllermeier E. Maximum likelihood estimation and coarse
data. In: In Proceedings SUM 2017, 11th International Conference on Scalable
Uncertainty Management, Granada, Spain. Springer; 2017:3-16.'
apa: Couso, I., Dubois, D., & Hüllermeier, E. (2017). Maximum likelihood estimation
and coarse data. In in Proceedings SUM 2017, 11th International Conference
on Scalable Uncertainty Management, Granada, Spain (pp. 3–16). Springer.
bibtex: '@inproceedings{Couso_Dubois_Hüllermeier_2017, title={Maximum likelihood
estimation and coarse data}, booktitle={in Proceedings SUM 2017, 11th International
Conference on Scalable Uncertainty Management, Granada, Spain}, publisher={Springer},
author={Couso, Ines and Dubois, D. and Hüllermeier, Eyke}, year={2017}, pages={3–16}
}'
chicago: Couso, Ines, D. Dubois, and Eyke Hüllermeier. “Maximum Likelihood Estimation
and Coarse Data.” In In Proceedings SUM 2017, 11th International Conference
on Scalable Uncertainty Management, Granada, Spain, 3–16. Springer, 2017.
ieee: I. Couso, D. Dubois, and E. Hüllermeier, “Maximum likelihood estimation and
coarse data,” in in Proceedings SUM 2017, 11th International Conference on
Scalable Uncertainty Management, Granada, Spain, 2017, pp. 3–16.
mla: Couso, Ines, et al. “Maximum Likelihood Estimation and Coarse Data.” In
Proceedings SUM 2017, 11th International Conference on Scalable Uncertainty Management,
Granada, Spain, Springer, 2017, pp. 3–16.
short: 'I. Couso, D. Dubois, E. Hüllermeier, in: In Proceedings SUM 2017, 11th International
Conference on Scalable Uncertainty Management, Granada, Spain, Springer, 2017,
pp. 3–16.'
date_created: 2019-11-21T16:38:39Z
date_updated: 2022-01-06T06:52:15Z
department:
- _id: '34'
- _id: '7'
- _id: '355'
language:
- iso: eng
page: 3-16
publication: in Proceedings SUM 2017, 11th International Conference on Scalable Uncertainty
Management, Granada, Spain
publisher: Springer
status: public
title: Maximum likelihood estimation and coarse data
type: conference
user_id: '49109'
year: '2017'
...
---
_id: '10204'
author:
- first_name: Ralph
full_name: Ewerth, Ralph
last_name: Ewerth
- first_name: M.
full_name: Springstein, M.
last_name: Springstein
- first_name: E.
full_name: Müller, E.
last_name: Müller
- first_name: A.
full_name: Balz, A.
last_name: Balz
- first_name: J.
full_name: Gehlhaar, J.
last_name: Gehlhaar
- first_name: T.
full_name: Naziyok, T.
last_name: Naziyok
- first_name: K.
full_name: Dembczynski, K.
last_name: Dembczynski
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Ewerth R, Springstein M, Müller E, et al. Estimating relative depth in single
images via rankboost. In: Proc. IEEE Int. Conf. on Multimedia and Expo (ICME
2017). ; 2017:919-924.'
apa: Ewerth, R., Springstein, M., Müller, E., Balz, A., Gehlhaar, J., Naziyok, T.,
… Hüllermeier, E. (2017). Estimating relative depth in single images via rankboost.
In Proc. IEEE Int. Conf. on Multimedia and Expo (ICME 2017) (pp. 919–924).
bibtex: '@inproceedings{Ewerth_Springstein_Müller_Balz_Gehlhaar_Naziyok_Dembczynski_Hüllermeier_2017,
title={Estimating relative depth in single images via rankboost}, booktitle={Proc.
IEEE Int. Conf. on Multimedia and Expo (ICME 2017)}, author={Ewerth, Ralph and
Springstein, M. and Müller, E. and Balz, A. and Gehlhaar, J. and Naziyok, T. and
Dembczynski, K. and Hüllermeier, Eyke}, year={2017}, pages={919–924} }'
chicago: Ewerth, Ralph, M. Springstein, E. Müller, A. Balz, J. Gehlhaar, T. Naziyok,
K. Dembczynski, and Eyke Hüllermeier. “Estimating Relative Depth in Single Images
via Rankboost.” In Proc. IEEE Int. Conf. on Multimedia and Expo (ICME 2017),
919–24, 2017.
ieee: R. Ewerth et al., “Estimating relative depth in single images via rankboost,”
in Proc. IEEE Int. Conf. on Multimedia and Expo (ICME 2017), 2017, pp.
919–924.
mla: Ewerth, Ralph, et al. “Estimating Relative Depth in Single Images via Rankboost.”
Proc. IEEE Int. Conf. on Multimedia and Expo (ICME 2017), 2017, pp. 919–24.
short: 'R. Ewerth, M. Springstein, E. Müller, A. Balz, J. Gehlhaar, T. Naziyok,
K. Dembczynski, E. Hüllermeier, in: Proc. IEEE Int. Conf. on Multimedia and Expo
(ICME 2017), 2017, pp. 919–924.'
date_created: 2019-06-07T15:18:24Z
date_updated: 2022-01-06T06:50:31Z
department:
- _id: '34'
- _id: '7'
- _id: '355'
language:
- iso: eng
page: 919-924
publication: Proc. IEEE Int. Conf. on Multimedia and Expo (ICME 2017)
status: public
title: Estimating relative depth in single images via rankboost
type: conference
user_id: '49109'
year: '2017'
...
---
_id: '10205'
author:
- first_name: Mohsen
full_name: Ahmadi Fahandar, Mohsen
last_name: Ahmadi Fahandar
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Ines
full_name: Couso, Ines
last_name: Couso
citation:
ama: 'Ahmadi Fahandar M, Hüllermeier E, Couso I. Statistical Inference for Incomplete
Ranking Data: The Case of Rank-Dependent Coarsening. In: Proc. 34th Int. Conf.
on Machine Learning (ICML 2017). ; 2017:1078-1087.'
apa: 'Ahmadi Fahandar, M., Hüllermeier, E., & Couso, I. (2017). Statistical
Inference for Incomplete Ranking Data: The Case of Rank-Dependent Coarsening.
In Proc. 34th Int. Conf. on Machine Learning (ICML 2017) (pp. 1078–1087).'
bibtex: '@inproceedings{Ahmadi Fahandar_Hüllermeier_Couso_2017, title={Statistical
Inference for Incomplete Ranking Data: The Case of Rank-Dependent Coarsening},
booktitle={Proc. 34th Int. Conf. on Machine Learning (ICML 2017)}, author={Ahmadi
Fahandar, Mohsen and Hüllermeier, Eyke and Couso, Ines}, year={2017}, pages={1078–1087}
}'
chicago: 'Ahmadi Fahandar, Mohsen, Eyke Hüllermeier, and Ines Couso. “Statistical
Inference for Incomplete Ranking Data: The Case of Rank-Dependent Coarsening.”
In Proc. 34th Int. Conf. on Machine Learning (ICML 2017), 1078–87, 2017.'
ieee: 'M. Ahmadi Fahandar, E. Hüllermeier, and I. Couso, “Statistical Inference
for Incomplete Ranking Data: The Case of Rank-Dependent Coarsening,” in Proc.
34th Int. Conf. on Machine Learning (ICML 2017), 2017, pp. 1078–1087.'
mla: 'Ahmadi Fahandar, Mohsen, et al. “Statistical Inference for Incomplete Ranking
Data: The Case of Rank-Dependent Coarsening.” Proc. 34th Int. Conf. on Machine
Learning (ICML 2017), 2017, pp. 1078–87.'
short: 'M. Ahmadi Fahandar, E. Hüllermeier, I. Couso, in: Proc. 34th Int. Conf.
on Machine Learning (ICML 2017), 2017, pp. 1078–1087.'
date_created: 2019-06-07T15:22:01Z
date_updated: 2022-01-06T06:50:31Z
department:
- _id: '34'
- _id: '7'
- _id: '355'
language:
- iso: eng
page: 1078-1087
publication: Proc. 34th Int. Conf. on Machine Learning (ICML 2017)
status: public
title: 'Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent Coarsening'
type: conference
user_id: '49109'
year: '2017'
...
---
_id: '10206'
author:
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Theodor
full_name: Lettmann, Theodor
id: '315'
last_name: Lettmann
orcid: 0000-0001-5859-2457
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Mohr F, Lettmann T, Hüllermeier E. Planning with Independent Task Networks.
In: Proc. 40th Annual German Conference on Advances in Artificial Intelligence
(KI 2017). ; 2017:193-206. doi:10.1007/978-3-319-67190-1_15'
apa: Mohr, F., Lettmann, T., & Hüllermeier, E. (2017). Planning with Independent
Task Networks. In Proc. 40th Annual German Conference on Advances in Artificial
Intelligence (KI 2017) (pp. 193–206). https://doi.org/10.1007/978-3-319-67190-1_15
bibtex: '@inproceedings{Mohr_Lettmann_Hüllermeier_2017, title={Planning with Independent
Task Networks}, DOI={10.1007/978-3-319-67190-1_15},
booktitle={Proc. 40th Annual German Conference on Advances in Artificial Intelligence
(KI 2017)}, author={Mohr, Felix and Lettmann, Theodor and Hüllermeier, Eyke},
year={2017}, pages={193–206} }'
chicago: Mohr, Felix, Theodor Lettmann, and Eyke Hüllermeier. “Planning with Independent
Task Networks.” In Proc. 40th Annual German Conference on Advances in Artificial
Intelligence (KI 2017), 193–206, 2017. https://doi.org/10.1007/978-3-319-67190-1_15.
ieee: F. Mohr, T. Lettmann, and E. Hüllermeier, “Planning with Independent Task
Networks,” in Proc. 40th Annual German Conference on Advances in Artificial
Intelligence (KI 2017), 2017, pp. 193–206.
mla: Mohr, Felix, et al. “Planning with Independent Task Networks.” Proc. 40th
Annual German Conference on Advances in Artificial Intelligence (KI 2017),
2017, pp. 193–206, doi:10.1007/978-3-319-67190-1_15.
short: 'F. Mohr, T. Lettmann, E. Hüllermeier, in: Proc. 40th Annual German Conference
on Advances in Artificial Intelligence (KI 2017), 2017, pp. 193–206.'
date_created: 2019-06-07T15:24:16Z
date_updated: 2022-01-06T06:50:31Z
ddc:
- '000'
department:
- _id: '7'
- _id: '34'
- _id: '355'
doi: 10.1007/978-3-319-67190-1_15
file:
- access_level: open_access
content_type: application/pdf
creator: lettmann
date_created: 2020-02-28T12:50:18Z
date_updated: 2020-02-28T12:50:18Z
file_id: '16157'
file_name: ki17.pdf
file_size: 374421
relation: main_file
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has_accepted_license: '1'
language:
- iso: eng
oa: '1'
page: 193-206
publication: Proc. 40th Annual German Conference on Advances in Artificial Intelligence
(KI 2017)
status: public
title: Planning with Independent Task Networks
type: conference
user_id: '315'
year: '2017'
...
---
_id: '10207'
author:
- first_name: M.
full_name: Czech, M.
last_name: Czech
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: M.-C.
full_name: Jakobs, M.-C.
last_name: Jakobs
- first_name: Heike
full_name: Wehrheim, Heike
id: '573'
last_name: Wehrheim
citation:
ama: 'Czech M, Hüllermeier E, Jakobs M-C, Wehrheim H. Predicting rankings of software
verification tools. In: Proc. 3rd ACM SIGSOFT Int. I Workshop on Software Analytics
(SWAN@ESEC/SIGSOFT FSE 2017. ; 2017:23-26.'
apa: Czech, M., Hüllermeier, E., Jakobs, M.-C., & Wehrheim, H. (2017). Predicting
rankings of software verification tools. In Proc. 3rd ACM SIGSOFT Int. I Workshop
on Software Analytics (SWAN@ESEC/SIGSOFT FSE 2017 (pp. 23–26).
bibtex: '@inproceedings{Czech_Hüllermeier_Jakobs_Wehrheim_2017, title={Predicting
rankings of software verification tools}, booktitle={Proc. 3rd ACM SIGSOFT Int.
I Workshop on Software Analytics (SWAN@ESEC/SIGSOFT FSE 2017}, author={Czech,
M. and Hüllermeier, Eyke and Jakobs, M.-C. and Wehrheim, Heike}, year={2017},
pages={23–26} }'
chicago: Czech, M., Eyke Hüllermeier, M.-C. Jakobs, and Heike Wehrheim. “Predicting
Rankings of Software Verification Tools.” In Proc. 3rd ACM SIGSOFT Int. I Workshop
on Software Analytics (SWAN@ESEC/SIGSOFT FSE 2017, 23–26, 2017.
ieee: M. Czech, E. Hüllermeier, M.-C. Jakobs, and H. Wehrheim, “Predicting rankings
of software verification tools,” in Proc. 3rd ACM SIGSOFT Int. I Workshop on
Software Analytics (SWAN@ESEC/SIGSOFT FSE 2017, 2017, pp. 23–26.
mla: Czech, M., et al. “Predicting Rankings of Software Verification Tools.” Proc.
3rd ACM SIGSOFT Int. I Workshop on Software Analytics (SWAN@ESEC/SIGSOFT FSE 2017,
2017, pp. 23–26.
short: 'M. Czech, E. Hüllermeier, M.-C. Jakobs, H. Wehrheim, in: Proc. 3rd ACM SIGSOFT
Int. I Workshop on Software Analytics (SWAN@ESEC/SIGSOFT FSE 2017, 2017, pp. 23–26.'
date_created: 2019-06-07T15:27:47Z
date_updated: 2022-01-06T06:50:31Z
department:
- _id: '34'
- _id: '7'
- _id: '355'
language:
- iso: eng
page: 23-26
publication: Proc. 3rd ACM SIGSOFT Int. I Workshop on Software Analytics (SWAN@ESEC/SIGSOFT
FSE 2017
status: public
title: Predicting rankings of software verification tools
type: conference
user_id: '49109'
year: '2017'
...
---
_id: '10208'
author:
- first_name: Ines
full_name: Couso, Ines
last_name: Couso
- first_name: D.
full_name: Dubois, D.
last_name: Dubois
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Couso I, Dubois D, Hüllermeier E. Maximum Likelihood Estimation and Coarse
Data. In: Proc. 11th Int. Conf. on Scalable Uncertainty Management (SUM 2017).
; 2017:3-16.'
apa: Couso, I., Dubois, D., & Hüllermeier, E. (2017). Maximum Likelihood Estimation
and Coarse Data. In Proc. 11th Int. Conf. on Scalable Uncertainty Management
(SUM 2017) (pp. 3–16).
bibtex: '@inproceedings{Couso_Dubois_Hüllermeier_2017, title={Maximum Likelihood
Estimation and Coarse Data}, booktitle={Proc. 11th Int. Conf. on Scalable Uncertainty
Management (SUM 2017)}, author={Couso, Ines and Dubois, D. and Hüllermeier, Eyke},
year={2017}, pages={3–16} }'
chicago: Couso, Ines, D. Dubois, and Eyke Hüllermeier. “Maximum Likelihood Estimation
and Coarse Data.” In Proc. 11th Int. Conf. on Scalable Uncertainty Management
(SUM 2017), 3–16, 2017.
ieee: I. Couso, D. Dubois, and E. Hüllermeier, “Maximum Likelihood Estimation and
Coarse Data,” in Proc. 11th Int. Conf. on Scalable Uncertainty Management (SUM
2017), 2017, pp. 3–16.
mla: Couso, Ines, et al. “Maximum Likelihood Estimation and Coarse Data.” Proc.
11th Int. Conf. on Scalable Uncertainty Management (SUM 2017), 2017, pp. 3–16.
short: 'I. Couso, D. Dubois, E. Hüllermeier, in: Proc. 11th Int. Conf. on Scalable
Uncertainty Management (SUM 2017), 2017, pp. 3–16.'
date_created: 2019-06-07T15:30:48Z
date_updated: 2022-01-06T06:50:31Z
department:
- _id: '34'
- _id: '7'
- _id: '355'
language:
- iso: eng
page: 3-16
publication: Proc. 11th Int. Conf. on Scalable Uncertainty Management (SUM 2017)
status: public
title: Maximum Likelihood Estimation and Coarse Data
type: conference
user_id: '49109'
year: '2017'
...
---
_id: '10209'
author:
- first_name: Mohsen
full_name: Ahmadi Fahandar, Mohsen
last_name: Ahmadi Fahandar
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Ahmadi Fahandar M, Hüllermeier E. Learning to Rank based on Analogical Reasoning.
In: Proc. AAAI 2017, 32nd AAAI Conference on Artificial Intelligence. ;
2017.'
apa: Ahmadi Fahandar, M., & Hüllermeier, E. (2017). Learning to Rank based on
Analogical Reasoning. In Proc. AAAI 2017, 32nd AAAI Conference on Artificial
Intelligence.
bibtex: '@inproceedings{Ahmadi Fahandar_Hüllermeier_2017, title={Learning to Rank
based on Analogical Reasoning}, booktitle={Proc. AAAI 2017, 32nd AAAI Conference
on Artificial Intelligence}, author={Ahmadi Fahandar, Mohsen and Hüllermeier,
Eyke}, year={2017} }'
chicago: Ahmadi Fahandar, Mohsen, and Eyke Hüllermeier. “Learning to Rank Based
on Analogical Reasoning.” In Proc. AAAI 2017, 32nd AAAI Conference on Artificial
Intelligence, 2017.
ieee: M. Ahmadi Fahandar and E. Hüllermeier, “Learning to Rank based on Analogical
Reasoning,” in Proc. AAAI 2017, 32nd AAAI Conference on Artificial Intelligence,
2017.
mla: Ahmadi Fahandar, Mohsen, and Eyke Hüllermeier. “Learning to Rank Based on Analogical
Reasoning.” Proc. AAAI 2017, 32nd AAAI Conference on Artificial Intelligence,
2017.
short: 'M. Ahmadi Fahandar, E. Hüllermeier, in: Proc. AAAI 2017, 32nd AAAI Conference
on Artificial Intelligence, 2017.'
date_created: 2019-06-07T15:33:14Z
date_updated: 2022-01-06T06:50:31Z
department:
- _id: '34'
- _id: '7'
- _id: '355'
language:
- iso: eng
publication: Proc. AAAI 2017, 32nd AAAI Conference on Artificial Intelligence
status: public
title: Learning to Rank based on Analogical Reasoning
type: conference
user_id: '49109'
year: '2017'
...
---
_id: '10212'
author:
- first_name: F.
full_name: Hoffmann, F.
last_name: Hoffmann
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: R.
full_name: Mikut, R.
last_name: Mikut
citation:
ama: 'Hoffmann F, Hüllermeier E, Mikut R. (Hrsg.) Proceedings 27. Workshop Computational
Intelligence, KIT Scientific Publishing, Karlsruhe, Germany 2017. In: ; 2017.'
apa: Hoffmann, F., Hüllermeier, E., & Mikut, R. (2017). (Hrsg.) Proceedings
27. Workshop Computational Intelligence, KIT Scientific Publishing, Karlsruhe,
Germany 2017.
bibtex: '@inproceedings{Hoffmann_Hüllermeier_Mikut_2017, title={(Hrsg.) Proceedings
27. Workshop Computational Intelligence, KIT Scientific Publishing, Karlsruhe,
Germany 2017}, author={Hoffmann, F. and Hüllermeier, Eyke and Mikut, R.}, year={2017}
}'
chicago: Hoffmann, F., Eyke Hüllermeier, and R. Mikut. “(Hrsg.) Proceedings 27.
Workshop Computational Intelligence, KIT Scientific Publishing, Karlsruhe, Germany
2017,” 2017.
ieee: F. Hoffmann, E. Hüllermeier, and R. Mikut, “(Hrsg.) Proceedings 27. Workshop
Computational Intelligence, KIT Scientific Publishing, Karlsruhe, Germany 2017,”
2017.
mla: Hoffmann, F., et al. (Hrsg.) Proceedings 27. Workshop Computational Intelligence,
KIT Scientific Publishing, Karlsruhe, Germany 2017. 2017.
short: 'F. Hoffmann, E. Hüllermeier, R. Mikut, in: 2017.'
date_created: 2019-06-07T15:46:10Z
date_updated: 2022-01-06T06:50:31Z
department:
- _id: '34'
- _id: '7'
- _id: '355'
language:
- iso: eng
status: public
title: (Hrsg.) Proceedings 27. Workshop Computational Intelligence, KIT Scientific
Publishing, Karlsruhe, Germany 2017
type: conference
user_id: '49109'
year: '2017'
...