@article{10319, author = {{Waegeman, W. and Dembczynski, K. and Jachnik, A. and Cheng, W. and Hüllermeier, Eyke}}, journal = {{in Journal of Machine Learning Research}}, pages = {{3333--3388}}, title = {{{On the Bayes-Optimality of F-Measure Maximizers}}}, volume = {{15}}, year = {{2015}}, } @article{10320, author = {{Hüllermeier, Eyke}}, journal = {{Fuzzy Sets and Systems}}, pages = {{292--299}}, title = {{{Does machine learning need fuzzy logic?}}}, volume = {{281}}, year = {{2015}}, } @article{10321, author = {{Shaker, Ammar and Hüllermeier, Eyke}}, journal = {{Neurocomputing}}, pages = {{250--264}}, title = {{{Recovery analysis for adaptive learning from non-stationary data streams: Experimental design and case study}}}, volume = {{150}}, year = {{2015}}, } @article{10322, author = {{Hüllermeier, Eyke}}, journal = {{Informatik Spektrum}}, number = {{6}}, pages = {{500--509}}, title = {{{From Knowledge-based to Data-driven fuzzy modeling-Development, criticism and alternative directions}}}, volume = {{38}}, year = {{2015}}, } @article{10323, author = {{Garcia-Jimenez, S. and Bustince, U. and Hüllermeier, Eyke and Mesiar, R. and Pal, N.R. and Pradera, A.}}, journal = {{IEEE Transactions on Fuzzy Systems}}, number = {{4}}, pages = {{1259--1273}}, title = {{{Overlap Indices: Construction of and Application of Interpolative Fuzzy Systems}}}, volume = {{23}}, year = {{2015}}, } @article{10324, author = {{Senge, Robin and Hüllermeier, Eyke}}, journal = {{IEEE Transactions on Fuzzy Systems}}, number = {{6}}, pages = {{2024--2033}}, title = {{{Fast Fuzzy Pattern Tree Learning of Classification}}}, volume = {{23}}, year = {{2015}}, } @article{24155, author = {{Basavaraju, Manu and Chandran, L Sunil and Rajendraprasad, Deepak and Ramaswamy, Arunselvan}}, journal = {{Graphs and Combinatorics}}, number = {{6}}, pages = {{1363--1382}}, publisher = {{Springer}}, title = {{{Rainbow connection number of graph power and graph products}}}, volume = {{30}}, year = {{2014}}, } @article{24156, author = {{Basavaraju, Manu and Chandran, L Sunil and Rajendraprasad, Deepak and Ramaswamy, Arunselvan}}, journal = {{Graphs and Combinatorics}}, number = {{2}}, pages = {{275--285}}, publisher = {{Springer}}, title = {{{Rainbow connection number and radius}}}, volume = {{30}}, year = {{2014}}, } @inproceedings{353, abstract = {{There are many technologies for the automation of processesthat deal with services; examples are service discovery and composition.Automation of these processes requires that the services are described semantically. However, semantically described services are currently not oronly rarely available, which limits the applicability of discovery and composition approaches. The systematic support for creating new semanticservices usable by automated technologies is an open problem.We tackle this problem with a template based approach: Domain independent templates are instantiated with domain specific services andboolean expressions. The obtained services have semantic descriptionswhose correctness directly follows from the correctness of the template.Besides the theory, we present experimental results for a service repository in which 85% of the services were generated automatically.}}, author = {{Mohr, Felix and Walther, Sven}}, booktitle = {{Proceedings of the 14th International Conference on Software Reuse (ICSR)}}, pages = {{188--203}}, title = {{{Template-based Generation of Semantic Services}}}, doi = {{10.1007/978-3-319-14130-5_14}}, year = {{2014}}, } @inproceedings{447, abstract = {{Automatic service composition is still a challengingtask. It is even more challenging when dealing witha dynamic market of services for end users. New servicesmay enter the market while other services are completelyremoved. Furthermore, end users are typically no experts in thedomain in which they formulate a request. As a consequence,ambiguous user requests will inevitably emerge and have tobe taken into account. To meet these challenges, we proposea new approach that combines automatic service compositionwith adaptive service recommendation. A best first backwardsearch algorithm produces solutions that are functional correctwith respect to user requests. An adaptive recommendationsystem supports the search algorithm in decision-making.Reinforcement Learning techniques enable the system to adjustits recommendation strategy over time based on user ratings.The integrated approach is described on a conceptional leveland demonstrated by means of an illustrative example fromthe image processing domain.}}, author = {{Jungmann, Alexander and Mohr, Felix and Kleinjohann, Bernd}}, booktitle = {{Proceedings of the 10th World Congress on Services (SERVICES)}}, pages = {{346--353}}, title = {{{Combining Automatic Service Composition with Adaptive Service Recommendation for Dynamic Markets of Services}}}, doi = {{10.1109/SERVICES.2014.68}}, year = {{2014}}, }