---
_id: '45695'
author:
- first_name: Sedjro Salomon
  full_name: Hotegni, Sedjro Salomon
  id: '97995'
  last_name: Hotegni
- first_name: Sepideh
  full_name: Mahabadi, Sepideh
  last_name: Mahabadi
- first_name: Ali
  full_name: Vakilian, Ali
  last_name: Vakilian
citation:
  ama: 'Hotegni SS, Mahabadi S, Vakilian A. Approximation Algorithms for Fair Range
    Clustering. In: <i>Proceedings of the 40th International Conference on Machine
    Learning, Honolulu, Hawaii, USA. PMLR 202, 2023.</i>'
  apa: Hotegni, S. S., Mahabadi, S., &#38; Vakilian, A. (n.d.). Approximation Algorithms
    for Fair Range Clustering. <i>Proceedings of the 40th International Conference
    on Machine Learning, Honolulu, Hawaii, USA. PMLR 202, 2023.</i> International
    Conference on Machine Learning, Honolulu, Hawaii, USA.
  bibtex: '@inproceedings{Hotegni_Mahabadi_Vakilian, title={Approximation Algorithms
    for Fair Range Clustering}, booktitle={Proceedings of the 40th International Conference
    on Machine Learning, Honolulu, Hawaii, USA. PMLR 202, 2023.}, author={Hotegni,
    Sedjro Salomon and Mahabadi, Sepideh and Vakilian, Ali} }'
  chicago: Hotegni, Sedjro Salomon, Sepideh Mahabadi, and Ali Vakilian. “Approximation
    Algorithms for Fair Range Clustering.” In <i>Proceedings of the 40th International
    Conference on Machine Learning, Honolulu, Hawaii, USA. PMLR 202, 2023.</i>, n.d.
  ieee: S. S. Hotegni, S. Mahabadi, and A. Vakilian, “Approximation Algorithms for
    Fair Range Clustering,” presented at the International Conference on Machine Learning,
    Honolulu, Hawaii, USA.
  mla: Hotegni, Sedjro Salomon, et al. “Approximation Algorithms for Fair Range Clustering.”
    <i>Proceedings of the 40th International Conference on Machine Learning, Honolulu,
    Hawaii, USA. PMLR 202, 2023.</i>
  short: 'S.S. Hotegni, S. Mahabadi, A. Vakilian, in: Proceedings of the 40th International
    Conference on Machine Learning, Honolulu, Hawaii, USA. PMLR 202, 2023., n.d.'
conference:
  end_date: 2023-07-29
  location: Honolulu, Hawaii, USA
  name: International Conference on Machine Learning
  start_date: 2023-07-23
date_created: 2023-06-20T22:29:33Z
date_updated: 2023-06-20T23:03:12Z
department:
- _id: '655'
keyword:
- Fair range clustering
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://openreview.net/forum?id=gBoKJT5JhM
oa: '1'
publication: Proceedings of the 40th International Conference on Machine Learning,
  Honolulu, Hawaii, USA. PMLR 202, 2023.
publication_status: accepted
status: public
title: Approximation Algorithms for Fair Range Clustering
type: conference
user_id: '97995'
year: '2023'
...
---
_id: '29934'
abstract:
- lang: eng
  text: Tire and road wear are a major source of emissions of nonexhaust particulate
    matter (PM) and make up the largest share of microplastics in the environment.
    To reduce tire wear through numerical optimization of a vehicle's suspension system,
    fast simulations of the representative usage of a vehicle are needed. Therefore,
    this contribution evaluates if instead of a full simulation of a representative
    test drive, only specific driving maneuvers resulting from a clustering of the
    driving data can be used to predict tire wear. As a measure for tire wear, the
    friction work between tire and road is calculated. It is shown that enough clusters
    result in negligible deviations between the total friction work of the full simulation
    and the cluster simulations as well as between the distributions of the friction
    work over the tire width. The calculation time can be reduced to about 1% of the
    full simulation.
author:
- first_name: Lars
  full_name: Muth, Lars
  id: '77313'
  last_name: Muth
  orcid: 0000-0002-2938-5616
- first_name: Christian
  full_name: Noll, Christian
  last_name: Noll
- first_name: Walter
  full_name: Sextro, Walter
  id: '21220'
  last_name: Sextro
citation:
  ama: 'Muth L, Noll C, Sextro W. Generation of a Reduced, Representative, Virtual
    Test Drive for Fast Evaluation of Tire Wear by Clustering of Driving Data. In:
    Orlova A, Cole D, eds. <i>Advances in Dynamics of Vehicles on Roads and Tracks
    II - Proceedings of the 27th Symposium of the International Association of Vehicle
    System Dynamics, IAVSD 2021</i>. Lecture Notes in Mechanical Engineering. Springer;
    2022. doi:<a href="https://doi.org/10.1007/978-3-031-07305-2_92">10.1007/978-3-031-07305-2_92</a>'
  apa: Muth, L., Noll, C., &#38; Sextro, W. (2022). Generation of a Reduced, Representative,
    Virtual Test Drive for Fast Evaluation of Tire Wear by Clustering of Driving Data.
    In A. Orlova &#38; D. Cole (Eds.), <i>Advances in Dynamics of Vehicles on Roads
    and Tracks II - Proceedings of the 27th Symposium of the International Association
    of Vehicle System Dynamics, IAVSD 2021</i>. Springer. <a href="https://doi.org/10.1007/978-3-031-07305-2_92">https://doi.org/10.1007/978-3-031-07305-2_92</a>
  bibtex: '@inproceedings{Muth_Noll_Sextro_2022, place={Cham}, series={Lecture Notes
    in Mechanical Engineering}, title={Generation of a Reduced, Representative, Virtual
    Test Drive for Fast Evaluation of Tire Wear by Clustering of Driving Data}, DOI={<a
    href="https://doi.org/10.1007/978-3-031-07305-2_92">10.1007/978-3-031-07305-2_92</a>},
    booktitle={Advances in Dynamics of Vehicles on Roads and Tracks II - Proceedings
    of the 27th Symposium of the International Association of Vehicle System Dynamics,
    IAVSD 2021}, publisher={Springer}, author={Muth, Lars and Noll, Christian and
    Sextro, Walter}, editor={Orlova, Anna and Cole, David}, year={2022}, collection={Lecture
    Notes in Mechanical Engineering} }'
  chicago: 'Muth, Lars, Christian Noll, and Walter Sextro. “Generation of a Reduced,
    Representative, Virtual Test Drive for Fast Evaluation of Tire Wear by Clustering
    of Driving Data.” In <i>Advances in Dynamics of Vehicles on Roads and Tracks II
    - Proceedings of the 27th Symposium of the International Association of Vehicle
    System Dynamics, IAVSD 2021</i>, edited by Anna Orlova and David Cole. Lecture
    Notes in Mechanical Engineering. Cham: Springer, 2022. <a href="https://doi.org/10.1007/978-3-031-07305-2_92">https://doi.org/10.1007/978-3-031-07305-2_92</a>.'
  ieee: 'L. Muth, C. Noll, and W. Sextro, “Generation of a Reduced, Representative,
    Virtual Test Drive for Fast Evaluation of Tire Wear by Clustering of Driving Data,”
    in <i>Advances in Dynamics of Vehicles on Roads and Tracks II - Proceedings of
    the 27th Symposium of the International Association of Vehicle System Dynamics,
    IAVSD 2021</i>, Saint Petersburg, Russia, 2022, doi: <a href="https://doi.org/10.1007/978-3-031-07305-2_92">10.1007/978-3-031-07305-2_92</a>.'
  mla: Muth, Lars, et al. “Generation of a Reduced, Representative, Virtual Test Drive
    for Fast Evaluation of Tire Wear by Clustering of Driving Data.” <i>Advances in
    Dynamics of Vehicles on Roads and Tracks II - Proceedings of the 27th Symposium
    of the International Association of Vehicle System Dynamics, IAVSD 2021</i>, edited
    by Anna Orlova and David Cole, Springer, 2022, doi:<a href="https://doi.org/10.1007/978-3-031-07305-2_92">10.1007/978-3-031-07305-2_92</a>.
  short: 'L. Muth, C. Noll, W. Sextro, in: A. Orlova, D. Cole (Eds.), Advances in
    Dynamics of Vehicles on Roads and Tracks II - Proceedings of the 27th Symposium
    of the International Association of Vehicle System Dynamics, IAVSD 2021, Springer,
    Cham, 2022.'
conference:
  end_date: 2021-08-19
  location: Saint Petersburg, Russia
  name: 27th IAVSD Symposium on Dynamics of Vehicles on Roads and Tracks, IAVSD 2021
  start_date: 2021-08-17
date_created: 2022-02-21T14:14:11Z
date_updated: 2022-08-23T11:55:07Z
department:
- _id: '151'
doi: 10.1007/978-3-031-07305-2_92
editor:
- first_name: Anna
  full_name: Orlova, Anna
  last_name: Orlova
- first_name: David
  full_name: Cole, David
  last_name: Cole
keyword:
- Tire Wear
- Vehicle Dynamics
- Clustering
- Virtual Test
language:
- iso: eng
main_file_link:
- url: https://link.springer.com/chapter/10.1007/978-3-031-07305-2_92
place: Cham
publication: Advances in Dynamics of Vehicles on Roads and Tracks II - Proceedings
  of the 27th Symposium of the International Association of Vehicle System Dynamics,
  IAVSD 2021
publication_identifier:
  eisbn:
  - 978-3-031-07305-2
  isbn:
  - 978-3-031-07304-5
publication_status: published
publisher: Springer
quality_controlled: '1'
series_title: Lecture Notes in Mechanical Engineering
status: public
title: Generation of a Reduced, Representative, Virtual Test Drive for Fast Evaluation
  of Tire Wear by Clustering of Driving Data
type: conference
user_id: '77313'
year: '2022'
...
---
_id: '27111'
abstract:
- lang: eng
  text: In the industry 4.0 era, there is a growing need to transform unstructured
    data acquired by a multitude of sources into information and subsequently into
    knowledge to improve the quality of manufactured products, to boost production,
    for predictive maintenance, etc. Data-driven approaches, such as machine learning
    techniques, are typically employed to model the underlying relationship from data.
    However, an increase in model accuracy with state-of-the-art methods, such as
    deep convolutional neural networks, results in less interpretability and transparency.
    Due to the ease of implementation, interpretation and transparency to both domain
    experts and non-experts, a rule-based method is proposed in this paper, for prognostics
    and health management (PHM) and specifically for diagnostics. The proposed method
    utilizes the most relevant sensor signals acquired via feature extraction and
    selection techniques and expert knowledge. As a case study, the presented method
    is evaluated on data from a real-world quality control set-up provided by the
    European prognostics and health management society (PHME) at the conference’s
    2021 data challenge. With the proposed method, our team took the third place,
    capable of successfully diagnosing different fault modes, irrespective of varying
    conditions.
author:
- first_name: Osarenren Kennedy
  full_name: Aimiyekagbon, Osarenren Kennedy
  id: '9557'
  last_name: Aimiyekagbon
- first_name: Lars
  full_name: Muth, Lars
  id: '77313'
  last_name: Muth
  orcid: 0000-0002-2938-5616
- first_name: Meike Claudia
  full_name: Wohlleben, Meike Claudia
  id: '43991'
  last_name: Wohlleben
  orcid: 0009-0009-9767-7168
- first_name: Amelie
  full_name: Bender, Amelie
  id: '54290'
  last_name: Bender
- first_name: Walter
  full_name: Sextro, Walter
  id: '21220'
  last_name: Sextro
citation:
  ama: 'Aimiyekagbon OK, Muth L, Wohlleben MC, Bender A, Sextro W. Rule-based Diagnostics
    of a Production Line. In: Do P, King S, Fink O, eds. <i>Proceedings of the European
    Conference of the PHM Society 2021</i>. Vol 6. ; 2021:527-536. doi:<a href="https://doi.org/10.36001/phme.2021.v6i1.3042">10.36001/phme.2021.v6i1.3042</a>'
  apa: Aimiyekagbon, O. K., Muth, L., Wohlleben, M. C., Bender, A., &#38; Sextro,
    W. (2021). Rule-based Diagnostics of a Production Line. In P. Do, S. King, &#38;
    O. Fink (Eds.), <i>Proceedings of the European Conference of the PHM Society 2021</i>
    (Vol. 6, Issue 1, pp. 527–536). <a href="https://doi.org/10.36001/phme.2021.v6i1.3042">https://doi.org/10.36001/phme.2021.v6i1.3042</a>
  bibtex: '@inproceedings{Aimiyekagbon_Muth_Wohlleben_Bender_Sextro_2021, title={Rule-based
    Diagnostics of a Production Line}, volume={6}, DOI={<a href="https://doi.org/10.36001/phme.2021.v6i1.3042">10.36001/phme.2021.v6i1.3042</a>},
    number={1}, booktitle={Proceedings of the European Conference of the PHM Society
    2021}, author={Aimiyekagbon, Osarenren Kennedy and Muth, Lars and Wohlleben, Meike
    Claudia and Bender, Amelie and Sextro, Walter}, editor={Do, Phuc and King, Steve
    and Fink, Olga}, year={2021}, pages={527–536} }'
  chicago: Aimiyekagbon, Osarenren Kennedy, Lars Muth, Meike Claudia Wohlleben, Amelie
    Bender, and Walter Sextro. “Rule-Based Diagnostics of a Production Line.” In <i>Proceedings
    of the European Conference of the PHM Society 2021</i>, edited by Phuc Do, Steve
    King, and Olga Fink, 6:527–36, 2021. <a href="https://doi.org/10.36001/phme.2021.v6i1.3042">https://doi.org/10.36001/phme.2021.v6i1.3042</a>.
  ieee: 'O. K. Aimiyekagbon, L. Muth, M. C. Wohlleben, A. Bender, and W. Sextro, “Rule-based
    Diagnostics of a Production Line,” in <i>Proceedings of the European Conference
    of the PHM Society 2021</i>, 2021, vol. 6, no. 1, pp. 527–536, doi: <a href="https://doi.org/10.36001/phme.2021.v6i1.3042">10.36001/phme.2021.v6i1.3042</a>.'
  mla: Aimiyekagbon, Osarenren Kennedy, et al. “Rule-Based Diagnostics of a Production
    Line.” <i>Proceedings of the European Conference of the PHM Society 2021</i>,
    edited by Phuc Do et al., vol. 6, no. 1, 2021, pp. 527–36, doi:<a href="https://doi.org/10.36001/phme.2021.v6i1.3042">10.36001/phme.2021.v6i1.3042</a>.
  short: 'O.K. Aimiyekagbon, L. Muth, M.C. Wohlleben, A. Bender, W. Sextro, in: P.
    Do, S. King, O. Fink (Eds.), Proceedings of the European Conference of the PHM
    Society 2021, 2021, pp. 527–536.'
conference:
  name: PHM Society European Conference
date_created: 2021-11-03T12:26:39Z
date_updated: 2023-09-22T09:13:01Z
department:
- _id: '151'
doi: 10.36001/phme.2021.v6i1.3042
editor:
- first_name: Phuc
  full_name: Do, Phuc
  last_name: Do
- first_name: Steve
  full_name: King, Steve
  last_name: King
- first_name: Olga
  full_name: Fink, Olga
  last_name: Fink
intvolume: '         6'
issue: '1'
keyword:
- PHME 2021
- Feature Selection Classification
- Feature Selection Clustering
- Interpretable Model
- Transparent Model
- Industry 4.0
- Real-World Diagnostics
- Quality Control
- Predictive Maintenance
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: http://papers.phmsociety.org/index.php/phme/article/download/3042/1812
oa: '1'
page: 527-536
publication: Proceedings of the European Conference of the PHM Society 2021
publication_status: published
quality_controlled: '1'
status: public
title: Rule-based Diagnostics of a Production Line
type: conference
user_id: '9557'
volume: 6
year: '2021'
...
---
_id: '2367'
abstract:
- lang: eng
  text: One of the most popular fuzzy clustering techniques is the fuzzy K-means algorithm
    (also known as fuzzy-c-means or FCM algorithm). In contrast to the K-means and
    K-median problem, the underlying fuzzy K-means problem has not been studied from
    a theoretical point of view. In particular, there are no algorithms with approximation
    guarantees similar to the famous K-means++ algorithm known for the fuzzy K-means
    problem. This work initiates the study of the fuzzy K-means problem from an algorithmic
    and complexity theoretic perspective. We show that optimal solutions for the fuzzy
    K-means problem cannot, in general, be expressed by radicals over the input points.
    Surprisingly, this already holds for simple inputs in one-dimensional space. Hence,
    one cannot expect to compute optimal solutions exactly. We give the first (1+eps)-approximation
    algorithms for the fuzzy K-means problem. First, we present a deterministic approximation
    algorithm whose runtime is polynomial in N and linear in the dimension D of the
    input set, given that K is constant, i.e. a polynomial time approximation scheme
    (PTAS) for fixed K. We achieve this result by showing that for each soft clustering
    there exists a hard clustering with similar properties. Second, by using techniques
    known from coreset constructions for the K-means problem, we develop a deterministic
    approximation algorithm that runs in time almost linear in N but exponential in
    the dimension D. We complement these results with a randomized algorithm which
    imposes some natural restrictions on the sought solution and whose runtime is
    comparable to some of the most efficient approximation algorithms for K-means,
    i.e. linear in the number of points and the dimension, but exponential in the
    number of clusters.
author:
- first_name: Johannes
  full_name: Blömer, Johannes
  id: '23'
  last_name: Blömer
- first_name: Sascha
  full_name: Brauer, Sascha
  id: '13291'
  last_name: Brauer
- first_name: Kathrin
  full_name: Bujna, Kathrin
  last_name: Bujna
citation:
  ama: 'Blömer J, Brauer S, Bujna K. A Theoretical Analysis of the Fuzzy K-Means Problem.
    In: <i>2016 IEEE 16th International Conference on Data Mining (ICDM)</i>. IEEE;
    2016:805-810. doi:<a href="https://doi.org/10.1109/icdm.2016.0094">10.1109/icdm.2016.0094</a>'
  apa: 'Blömer, J., Brauer, S., &#38; Bujna, K. (2016). A Theoretical Analysis of
    the Fuzzy K-Means Problem. In <i>2016 IEEE 16th International Conference on Data
    Mining (ICDM)</i> (pp. 805–810). Barcelona, Spain: IEEE. <a href="https://doi.org/10.1109/icdm.2016.0094">https://doi.org/10.1109/icdm.2016.0094</a>'
  bibtex: '@inproceedings{Blömer_Brauer_Bujna_2016, title={A Theoretical Analysis
    of the Fuzzy K-Means Problem}, DOI={<a href="https://doi.org/10.1109/icdm.2016.0094">10.1109/icdm.2016.0094</a>},
    booktitle={2016 IEEE 16th International Conference on Data Mining (ICDM)}, publisher={IEEE},
    author={Blömer, Johannes and Brauer, Sascha and Bujna, Kathrin}, year={2016},
    pages={805–810} }'
  chicago: Blömer, Johannes, Sascha Brauer, and Kathrin Bujna. “A Theoretical Analysis
    of the Fuzzy K-Means Problem.” In <i>2016 IEEE 16th International Conference on
    Data Mining (ICDM)</i>, 805–10. IEEE, 2016. <a href="https://doi.org/10.1109/icdm.2016.0094">https://doi.org/10.1109/icdm.2016.0094</a>.
  ieee: J. Blömer, S. Brauer, and K. Bujna, “A Theoretical Analysis of the Fuzzy K-Means
    Problem,” in <i>2016 IEEE 16th International Conference on Data Mining (ICDM)</i>,
    Barcelona, Spain, 2016, pp. 805–810.
  mla: Blömer, Johannes, et al. “A Theoretical Analysis of the Fuzzy K-Means Problem.”
    <i>2016 IEEE 16th International Conference on Data Mining (ICDM)</i>, IEEE, 2016,
    pp. 805–10, doi:<a href="https://doi.org/10.1109/icdm.2016.0094">10.1109/icdm.2016.0094</a>.
  short: 'J. Blömer, S. Brauer, K. Bujna, in: 2016 IEEE 16th International Conference
    on Data Mining (ICDM), IEEE, 2016, pp. 805–810.'
conference:
  end_date: 2016-12-15
  location: Barcelona, Spain
  name: IEEE 16th International Conference on Data Mining (ICDM)
  start_date: 2016-12-12
date_created: 2018-04-17T11:46:07Z
date_updated: 2022-01-06T06:55:58Z
department:
- _id: '64'
doi: 10.1109/icdm.2016.0094
keyword:
- unsolvability by radicals
- clustering
- fuzzy k-means
- probabilistic method
- approximation algorithms
- randomized algorithms
language:
- iso: eng
page: 805-810
publication: 2016 IEEE 16th International Conference on Data Mining (ICDM)
publication_identifier:
  isbn:
  - '9781509054732'
publication_status: published
publisher: IEEE
status: public
title: A Theoretical Analysis of the Fuzzy K-Means Problem
type: conference
user_id: '25078'
year: '2016'
...
---
_id: '2990'
author:
- first_name: Marcel R.
  full_name: Ackermann, Marcel R.
  last_name: Ackermann
- first_name: Johannes
  full_name: Blömer, Johannes
  id: '23'
  last_name: Blömer
- first_name: Christian
  full_name: Sohler, Christian
  last_name: Sohler
citation:
  ama: Ackermann MR, Blömer J, Sohler C. Clustering for Metric and Nonmetric Distance
    Measures. <i>ACM Trans Algorithms</i>. 2010;(4):59:1--59:26. doi:<a href="https://doi.org/10.1145/1824777.1824779">10.1145/1824777.1824779</a>
  apa: Ackermann, M. R., Blömer, J., &#38; Sohler, C. (2010). Clustering for Metric
    and Nonmetric Distance Measures. <i>ACM Trans. Algorithms</i>, (4), 59:1--59:26.
    <a href="https://doi.org/10.1145/1824777.1824779">https://doi.org/10.1145/1824777.1824779</a>
  bibtex: '@article{Ackermann_Blömer_Sohler_2010, title={Clustering for Metric and
    Nonmetric Distance Measures}, DOI={<a href="https://doi.org/10.1145/1824777.1824779">10.1145/1824777.1824779</a>},
    number={4}, journal={ACM Trans. Algorithms}, author={Ackermann, Marcel R. and
    Blömer, Johannes and Sohler, Christian}, year={2010}, pages={59:1--59:26} }'
  chicago: 'Ackermann, Marcel R., Johannes Blömer, and Christian Sohler. “Clustering
    for Metric and Nonmetric Distance Measures.” <i>ACM Trans. Algorithms</i>, no.
    4 (2010): 59:1--59:26. <a href="https://doi.org/10.1145/1824777.1824779">https://doi.org/10.1145/1824777.1824779</a>.'
  ieee: M. R. Ackermann, J. Blömer, and C. Sohler, “Clustering for Metric and Nonmetric
    Distance Measures,” <i>ACM Trans. Algorithms</i>, no. 4, pp. 59:1--59:26, 2010.
  mla: Ackermann, Marcel R., et al. “Clustering for Metric and Nonmetric Distance
    Measures.” <i>ACM Trans. Algorithms</i>, no. 4, 2010, pp. 59:1--59:26, doi:<a
    href="https://doi.org/10.1145/1824777.1824779">10.1145/1824777.1824779</a>.
  short: M.R. Ackermann, J. Blömer, C. Sohler, ACM Trans. Algorithms (2010) 59:1--59:26.
date_created: 2018-06-05T07:52:41Z
date_updated: 2022-01-06T06:58:50Z
department:
- _id: '64'
doi: 10.1145/1824777.1824779
issue: '4'
keyword:
- k-means clustering
- k-median clustering
- Approximation algorithm
- Bregman divergences
- Itakura-Saito divergence
- Kullback-Leibler divergence
- Mahalanobis distance
- random sampling
page: 59:1--59:26
publication: ACM Trans. Algorithms
publication_identifier:
  issn:
  - 1549-6325
publication_status: published
status: public
title: Clustering for Metric and Nonmetric Distance Measures
type: journal_article
user_id: '25078'
year: '2010'
...
---
_id: '6508'
abstract:
- lang: eng
  text: 'In this paper, we present a framework that supports experimenting with evolutionary
    hardware design. We describe the framework''s modules for composing evolutionary
    optimizers and for setting up, controlling, and analyzing experiments. Two case
    studies demonstrate the usefulness of the framework: evolution of hash functions
    and evolution based on pre-engineered circuits.'
author:
- first_name: Paul
  full_name: Kaufmann, Paul
  last_name: Kaufmann
- first_name: Marco
  full_name: Platzner, Marco
  id: '398'
  last_name: Platzner
citation:
  ama: 'Kaufmann P, Platzner M. MOVES: A Modular Framework for Hardware Evolution.
    In: <i>Second NASA/ESA Conference on Adaptive Hardware and Systems (AHS 2007)</i>.
    IEEE; 2007:447-454. doi:<a href="https://doi.org/10.1109/ahs.2007.73">10.1109/ahs.2007.73</a>'
  apa: 'Kaufmann, P., &#38; Platzner, M. (2007). MOVES: A Modular Framework for Hardware
    Evolution. In <i>Second NASA/ESA Conference on Adaptive Hardware and Systems (AHS
    2007)</i> (pp. 447–454). Edinburgh, UK: IEEE. <a href="https://doi.org/10.1109/ahs.2007.73">https://doi.org/10.1109/ahs.2007.73</a>'
  bibtex: '@inproceedings{Kaufmann_Platzner_2007, title={MOVES: A Modular Framework
    for Hardware Evolution}, DOI={<a href="https://doi.org/10.1109/ahs.2007.73">10.1109/ahs.2007.73</a>},
    booktitle={Second NASA/ESA Conference on Adaptive Hardware and Systems (AHS 2007)},
    publisher={IEEE}, author={Kaufmann, Paul and Platzner, Marco}, year={2007}, pages={447–454}
    }'
  chicago: 'Kaufmann, Paul, and Marco Platzner. “MOVES: A Modular Framework for Hardware
    Evolution.” In <i>Second NASA/ESA Conference on Adaptive Hardware and Systems
    (AHS 2007)</i>, 447–54. IEEE, 2007. <a href="https://doi.org/10.1109/ahs.2007.73">https://doi.org/10.1109/ahs.2007.73</a>.'
  ieee: 'P. Kaufmann and M. Platzner, “MOVES: A Modular Framework for Hardware Evolution,”
    in <i>Second NASA/ESA Conference on Adaptive Hardware and Systems (AHS 2007)</i>,
    Edinburgh, UK, 2007, pp. 447–454.'
  mla: 'Kaufmann, Paul, and Marco Platzner. “MOVES: A Modular Framework for Hardware
    Evolution.” <i>Second NASA/ESA Conference on Adaptive Hardware and Systems (AHS
    2007)</i>, IEEE, 2007, pp. 447–54, doi:<a href="https://doi.org/10.1109/ahs.2007.73">10.1109/ahs.2007.73</a>.'
  short: 'P. Kaufmann, M. Platzner, in: Second NASA/ESA Conference on Adaptive Hardware
    and Systems (AHS 2007), IEEE, 2007, pp. 447–454.'
conference:
  end_date: 2007-08-08
  location: Edinburgh, UK
  name: Second NASA/ESA Conference on Adaptive Hardware and Systems (AHS 2007)
  start_date: 2007-08-05
date_created: 2019-01-08T09:52:43Z
date_updated: 2022-01-06T07:03:08Z
department:
- _id: '78'
doi: 10.1109/ahs.2007.73
keyword:
- integrated circuit design
- hardware evolution
- evolutionary hardware design
- evolutionary optimizers
- hash functions
- preengineered circuits
- Hardware
- Circuits
- Design optimization
- Visualization
- Genetic programming
- Genetic mutations
- Clustering algorithms
- Biological cells
- Field programmable gate arrays
- Routing
language:
- iso: eng
page: 447-454
publication: Second NASA/ESA Conference on Adaptive Hardware and Systems (AHS 2007)
publication_identifier:
  isbn:
  - 076952866X
  - '9780769528663'
publication_status: published
publisher: IEEE
status: public
title: 'MOVES: A Modular Framework for Hardware Evolution'
type: conference
user_id: '3118'
year: '2007'
...
---
_id: '11778'
abstract:
- lang: eng
  text: In this paper, it is shown that a correlation criterion is the appropriate
    criterion for bottom-up clustering to obtain broad phonetic class regression trees
    for maximum likelihood linear regression (MLLR)-based speaker adaptation. The
    correlation structure among speech units is estimated on the speaker-independent
    training data. In adaptation experiments the tree outperformed a regression tree
    obtained from clustering according to closeness in acoustic space and achieved
    results comparable with those of a manually designed broad phonetic class tree
author:
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: Haeb-Umbach R. Automatic generation of phonetic regression class trees for
    MLLR adaptation. <i>IEEE Transactions on Speech and Audio Processing</i>. 2001;9(3):299-302.
    doi:<a href="https://doi.org/10.1109/89.906003">10.1109/89.906003</a>
  apa: Haeb-Umbach, R. (2001). Automatic generation of phonetic regression class trees
    for MLLR adaptation. <i>IEEE Transactions on Speech and Audio Processing</i>,
    <i>9</i>(3), 299–302. <a href="https://doi.org/10.1109/89.906003">https://doi.org/10.1109/89.906003</a>
  bibtex: '@article{Haeb-Umbach_2001, title={Automatic generation of phonetic regression
    class trees for MLLR adaptation}, volume={9}, DOI={<a href="https://doi.org/10.1109/89.906003">10.1109/89.906003</a>},
    number={3}, journal={IEEE Transactions on Speech and Audio Processing}, author={Haeb-Umbach,
    Reinhold}, year={2001}, pages={299–302} }'
  chicago: 'Haeb-Umbach, Reinhold. “Automatic Generation of Phonetic Regression Class
    Trees for MLLR Adaptation.” <i>IEEE Transactions on Speech and Audio Processing</i>
    9, no. 3 (2001): 299–302. <a href="https://doi.org/10.1109/89.906003">https://doi.org/10.1109/89.906003</a>.'
  ieee: R. Haeb-Umbach, “Automatic generation of phonetic regression class trees for
    MLLR adaptation,” <i>IEEE Transactions on Speech and Audio Processing</i>, vol.
    9, no. 3, pp. 299–302, 2001.
  mla: Haeb-Umbach, Reinhold. “Automatic Generation of Phonetic Regression Class Trees
    for MLLR Adaptation.” <i>IEEE Transactions on Speech and Audio Processing</i>,
    vol. 9, no. 3, 2001, pp. 299–302, doi:<a href="https://doi.org/10.1109/89.906003">10.1109/89.906003</a>.
  short: R. Haeb-Umbach, IEEE Transactions on Speech and Audio Processing 9 (2001)
    299–302.
date_created: 2019-07-12T05:28:04Z
date_updated: 2022-01-06T06:51:08Z
department:
- _id: '54'
doi: 10.1109/89.906003
intvolume: '         9'
issue: '3'
keyword:
- acoustic space
- adaptation experiments
- automatic generation
- bottom-up clustering
- broad phonetic class regression trees
- correlation criterion
- correlation methods
- maximum likelihood estimation
- maximum likelihood linear regression based speaker adaptation
- MLLR adaptation
- pattern clustering
- phonetic regression class trees
- speaker-independent training data
- speech recognition
- speech units
- statistical analysis
- trees (mathematics)
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2001/Ha01.pdf
oa: '1'
page: 299-302
publication: IEEE Transactions on Speech and Audio Processing
status: public
title: Automatic generation of phonetic regression class trees for MLLR adaptation
type: journal_article
user_id: '44006'
volume: 9
year: '2001'
...