@inproceedings{15113, author = {{Nasiri, N. and Fober, T. and Senge, Robin and Hüllermeier, Eyke}}, booktitle = {{in Proceedings IFSA-2013 World Congress of the International Fuzzy Systems Association, Edmonton, Canada}}, pages = {{715--721}}, title = {{{Fuzzy Pattern Trees as an alternative to rule-based fuzzy systems: Knowledge-driven, data-driven and hybrid modeling of colour yield in poyester dyeing, Edmonton, Canada}}}, year = {{2013}}, } @inproceedings{15162, author = {{Böttcher, Stefan and Bültmann, Alexander and Hartel, Rita and Schlüßler, Jonathan}}, booktitle = {{International Conference on Database and Expert Systems Applications}}, pages = {{189--202}}, publisher = {{Springer}}, title = {{{Implementing Efficient Updates in Compressed Big Text Databases}}}, year = {{2013}}, } @article{15282, author = {{Alford, Jennifer Ginger and Jacob, Lucas and Dietz, Paul}}, journal = {{IEEE computer graphics and applications}}, number = {{6}}, pages = {{9--13}}, publisher = {{IEEE}}, title = {{{Animatronics Workshop: A Theater+ Engineering Collaboration at a High School}}}, doi = {{10.1109/MCG.2013.86}}, volume = {{33}}, year = {{2013}}, } @inproceedings{15284, author = {{Arens, Stephan and Bolte, Matthias and Domik, Gitta}}, booktitle = {{Vision, Modeling & Visualization}}, editor = {{Bronstein , Michael and Favre , Jean and Hormann, Kai }}, isbn = {{978-3-905674-51-4}}, publisher = {{The Eurographics Association}}, title = {{{Visualizing Dissections of the Heart in a Dataflow-based Shader Framework for Volume Rendering}}}, doi = {{10.2312/PE.VMV.VMV13.231-232}}, year = {{2013}}, } @article{16044, author = {{Heider, D. and Senge, Robin and Cheng, W. and Hüllermeier, Eyke}}, journal = {{Bioinformatics}}, number = {{16}}, pages = {{1946--1952}}, title = {{{Multilabel classification for exploiting cross-resistance information in HIV-1 drug resistence prediction}}}, volume = {{29}}, year = {{2013}}, } @article{16081, author = {{Bösner, S. and Bönisch, K. and Haasenritter , J. and Schlegel, P. and Hüllermeier, Eyke and Donner-Banzhoff, N.}}, journal = {{BMC Family Practice}}, number = {{1}}, pages = {{154--162}}, title = {{{Chest pain in primary care: is the localization of pain diagnostically helpful in the critical evaluation of patients? A cross sectional study. }}}, volume = {{14}}, year = {{2013}}, } @article{16086, author = {{Haasenritter, J. and Viniol, A. and Becker, A. and Bösner, S. and Hüllermeier, Eyke and Senge, Robin and Donner-Banzhoff, N.}}, journal = {{Zeitschrift für Evidenz, Fortbildung und Qualität im Gesundheitswesen (ZEFQ)}}, pages = {{585--591}}, title = {{{Diagnose im Kontext - eine erweiterte Perspektive}}}, volume = {{107}}, year = {{2013}}, } @article{16123, author = {{Shaker, A. and Senge, R. and Hüllermeier, Eyke}}, journal = {{Information Sciences}}, pages = {{34--45}}, title = {{{Evolving fuzzy pattern trees for binary classification on data streams}}}, volume = {{220}}, year = {{2013}}, } @inproceedings{16393, abstract = {{Many 3D scenes (e.g. generated from CAD data) are composed of a multitude of objects that are nested in each other. A showroom, for instance, may contain multiple cars and every car has a gearbox with many gearwheels located inside. Because the objects occlude each other, only few are visible from outside. We present a new technique, Spherical Visibility Sampling (SVS), for real-time 3D rendering of such -- possibly highly complex -- scenes. SVS exploits the occlusion and annotates hierarchically structured objects with directional visibility information in a preprocessing step. For different directions, the directional visibility encodes which objects of a scene's region are visible from the outside of the regions' enclosing bounding sphere. Since there is no need to store a separate view space subdivision as in most techniques based on preprocessed visibility, a small memory footprint is achieved. Using the directional visibility information for an interactive walkthrough, the potentially visible objects can be retrieved very efficiently without the need for further visibility tests. Our evaluation shows that using SVS allows to preprocess complex 3D scenes fast and to visualize them in real time (e.g. a Power Plant model and five animated Boeing 777 models with billions of triangles). Because SVS does not require hardware support for occlusion culling during rendering, it is even applicable for rendering large scenes on mobile devices.}}, author = {{Eikel, Benjamin and Jähn, Claudius and Fischer, Matthias and Meyer auf der Heide, Friedhelm}}, booktitle = {{Computer Graphics Forum}}, issn = {{0167-7055}}, number = {{4}}, pages = {{49--58}}, title = {{{Spherical Visibility Sampling}}}, doi = {{10.1111/cgf.12150}}, volume = {{32}}, year = {{2013}}, } @inbook{16406, abstract = {{In order to evaluate the efficiency of algorithms for real-time 3D rendering, different properties like rendering time, occluded triangles, or image quality, need to be investigated. Since these properties depend on the position of the camera, usually some camera path is chosen, along which the measurements are performed. As those measurements cover only a small part of the scene, this approach hardly allows drawing conclusions regarding the algorithm's properties at arbitrary positions in the scene. The presented method allows the systematic and position-independent evaluation of rendering algorithms. It uses an adaptive sampling approach to approximate the distribution of a property (like rendering time) for all positions in the scene. This approximation can be visualized to produce an intuitive impression of the algorithm's behavior or be statistically analyzed for objectively rating and comparing algorithms. We demonstrate our method by evaluating performance aspects of a known occlusion culling algorithm. }}, author = {{Jähn, Claudius and Eikel, Benjamin and Fischer, Matthias and Petring, Ralf and Meyer auf der Heide, Friedhelm}}, booktitle = {{Advances in Visual Computing}}, isbn = {{9783642419133}}, issn = {{0302-9743}}, title = {{{Evaluation of Rendering Algorithms Using Position-Dependent Scene Properties}}}, doi = {{10.1007/978-3-642-41914-0_12}}, year = {{2013}}, } @inbook{16407, abstract = {{Many virtual 3D scenes, especially those that are large, are not structured evenly. For such heterogeneous data, there is no single algorithm that is able to render every scene type at each position fast and with the same high image quality. For a small set of scenes, this situation can be improved if different rendering algorithms are manually assigned to particular parts of the scene by an experienced user. We introduce the Multi-Algorithm-Rendering method. It automatically deploys different rendering algorithms simultaneously for a broad range of scene types. The method divides the scene into subregions and measures the behavior of different algorithms for each region in a preprocessing step. During runtime, this data is utilized to compute an estimate for the quality and running time of the available rendering algorithms from the observer's point of view. By solving an optimizing problem, the image quality can be optimized by an assignment of algorithms to regions while keeping the frame rate almost constant. }}, author = {{Petring, Ralf and Eikel, Benjamin and Jähn, Claudius and Fischer, Matthias and Meyer auf der Heide, Friedhelm}}, booktitle = {{Advances in Visual Computing}}, isbn = {{9783642419133}}, issn = {{0302-9743}}, title = {{{Real-Time 3D Rendering of Heterogeneous Scenes}}}, doi = {{10.1007/978-3-642-41914-0_44}}, year = {{2013}}, } @inproceedings{13115, author = {{Szarvas, G. and Busa-Fekete, Robert and Hüllermeier, Eyke}}, booktitle = {{In Proceedings EMNLP-2013 Conference on Empirical Methods in Natural Language Processing, Seattle, USA}}, title = {{{Learning to rank lexical substitutions}}}, year = {{2013}}, } @inproceedings{13116, author = {{Dembczynski, K. and Jachnik, A. and Kotlowski, W. and Waegeman, W. and Hüllermeier, Eyke}}, booktitle = {{in Proceedings ICML-2013, 30th International Conference on Machine Learning, Atlanta, USA}}, editor = {{Dasgupta, S. and McAllester, D.}}, pages = {{1130--1138}}, title = {{{Optimizing the F-measure in multi-label classification: Plug-in rule approach versus structured loss minimization}}}, year = {{2013}}, } @inproceedings{13117, author = {{Busa-Fekete, Robert and Szoreny, B. and Weng, P. and Cheng, W. and Hüllermeier, Eyke}}, booktitle = {{in Proceedings ICML-2013, 30th International Conference on Machine Learning, Atlanta, USA}}, editor = {{Dasgupta, S. and McAllester, D.}}, pages = {{1094--1102}}, title = {{{Top-k selection based on adaptive sampling of noisy preferences}}}, year = {{2013}}, } @inproceedings{13118, author = {{Hüllermeier, Eyke and Cheng, W.}}, booktitle = {{in Proceedings IJCAI-13, 23rd international Joint Conference on Artificial Intelligence, Beijing, China}}, editor = {{Rossi, F.}}, pages = {{3012--3016}}, publisher = {{AAAI Press}}, title = {{{Preference-based CBR: General ideas and basic principles}}}, year = {{2013}}, } @inproceedings{13119, author = {{Henzgen, Sascha and Strickert, M. and Hüllermeier, Eyke}}, booktitle = {{in Proceedings CORES 2013, 8th International Conference on Computer Recognition Systems, Wroclaw, Poland}}, editor = {{Burduk, R. and Jackowski, K. and Kurzynski, M. and Wozniak, M. and Zolnierek, A.}}, pages = {{279--288}}, publisher = {{Springer}}, title = {{{Rule chains for visualizing evolving fuzzy rule-based systems}}}, year = {{2013}}, } @inproceedings{13190, author = {{Shaker, Ammar and Hüllermeier, Eyke}}, booktitle = {{in Proceedings CORES 2013, 8th International Conference on Computer Recognition Systems, Wroclaw, Poland}}, editor = {{Burduk, R. and Jackowski, K. and Kurzynski, M. and Wozniak, W. and Zolnierek, A.}}, pages = {{289--298}}, publisher = {{Springer}}, title = {{{Recovery analysis for adaptive learning from non-stationary data streams}}}, year = {{2013}}, } @inproceedings{13645, author = {{Graf, Tobias and Schäfers, Lars and Platzner, Marco}}, booktitle = {{Proceedings of the International Conference on Computers and Games (CG)}}, publisher = {{Springer}}, title = {{{On Semeai Detection in Monte-Carlo Go.}}}, year = {{2013}}, } @inbook{46385, abstract = {{In many applications one is faced with the problem that multiple objectives have to be optimized at the same time. Since typically the solution set of such multi-objective optimization problems forms a manifold which cannot be computed analytically, one is in many cases interested in a suitable finite size approximation of this set. One widely used approach is to find a representative set that maximizes the dominated hypervolume that is defined by the images in objective space of these solutions and a given reference point. In this paper, we propose a new point-wise iterative search procedure, Hypervolume Directed Search (HVDS), that aims to increase the hypervolume of a given point in an archive for bi-objective unconstrained optimization problems. We present the HVDS both as a standalone algorithm and as a local searcher within a specialized evolutionary algorithm. Numerical results confirm the strength of the novel approach.}}, author = {{Sosa, Hernández V and Schütze, O and Rudolph, G and Trautmann, Heike}}, booktitle = {{EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation IV}}, editor = {{Emmerich, M and Deutz, A and Schuetze, O and Bäck, T and Tantar, A and Moral, PD and Legrand, P and Bouvry, P and Coello, CA}}, isbn = {{978-3-319-01127-1}}, pages = {{189–205}}, publisher = {{Springer International Publishing}}, title = {{{The Directed Search Method for Pareto Front Approximations with Maximum Dominated Hypervolume}}}, doi = {{10.1007/978-3-319-01128-8_13}}, volume = {{227}}, year = {{2013}}, } @inbook{46386, abstract = {{The averaged Hausdorff distance Δ p is a performance indicator in multi-objective evolutionary optimization which simultaneously takes into account proximity to the true Pareto front and uniform spread of solutions. Recently, the multi-objective evolutionary algorithm Δ p -EMOA was introduced which successfully generates evenly spaced Pareto front approximations for bi-objective problems by integrating an external archiving strategy into the SMS-EMOA based on Δ p . In this work a conceptual generalization of the Δ p -EMOA for higher objective space dimensions is presented and experimentally compared to state-of-the art EMOA as well as specialized EMOA variants on three-dimensional optimization problems.}}, author = {{Trautmann, Heike and Rudolph, G and Dominguez-Medina, C and Schütze, O}}, booktitle = {{EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation II}}, editor = {{Schütze, O and Coello, Coello CA and Tantar, A and Tantar, E and Bouvry, P and Del, Moral P and Legrand, P}}, isbn = {{978-3-642-31518-3}}, pages = {{89–105}}, publisher = {{Springer Berlin Heidelberg}}, title = {{{Finding Evenly Spaced Pareto Fronts for Three-Objective Optimization Problems}}}, doi = {{10.1007/978-3-642-31519-0_6}}, volume = {{175}}, year = {{2013}}, }