@inproceedings{10295, author = {{Fürnkranz, J. and Hüllermeier, Eyke and Rudin, Cynthia and Slowinski, Roman and Sanner, Scott}}, number = {{3}}, pages = {{1--27}}, title = {{{Preference Learning (Dagstuhl Seminar 14101) Dagstuhl Reports}}}, volume = {{4}}, year = {{2014}}, } @article{10296, author = {{Shaker, Ammar and Hüllermeier, Eyke}}, journal = {{Applied Mathematics and Computer Science}}, number = {{1}}, pages = {{199--212}}, title = {{{Survival analysis on data streams: Analyzing temporal events in dynamically changing environments}}}, volume = {{24}}, year = {{2014}}, } @article{10297, author = {{Hoffmann, F. and Hüllermeier, Eyke and Kroll, A.}}, journal = {{Computational Intelligence Automatisierungstechnik}}, number = {{10}}, pages = {{685--686}}, title = {{{Ausgewählte Beiträge des GMA-Fachausschusses 5.14}}}, volume = {{62}}, year = {{2014}}, } @article{10298, author = {{Calders, T. and Esposito, F. and Hüllermeier, Eyke and Meo, R.}}, journal = {{Data Min. Knowledge Discovery}}, number = {{5-6}}, pages = {{1129--1133}}, title = {{{Guest editors`introduction:special issue of the ECML/PKDD 2014 journal track}}}, volume = {{28}}, year = {{2014}}, } @article{10299, author = {{Henzgen, Sascha and Strickert, M. and Hüllermeier, Eyke}}, journal = {{Evolving Systems}}, number = {{3}}, pages = {{175--191}}, title = {{{Visualization of evolving fuzzy rule-based systems}}}, volume = {{5}}, year = {{2014}}, } @article{10308, author = {{Hüllermeier, Eyke}}, journal = {{Int. J. Approx. Reasoning}}, number = {{7}}, pages = {{1519--1534}}, title = {{{Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization}}}, volume = {{55}}, year = {{2014}}, } @article{10310, author = {{Strickert, M. and Bunte, K. and Schleif, F.- M. and Hüllermeier, Eyke}}, journal = {{Neurocomputing}}, pages = {{97--109}}, title = {{{Correlation-based embedding of pairwise score data}}}, volume = {{141}}, year = {{2014}}, } @article{10311, author = {{Senge, Robin and Bösner, S. and Dembczynski, K. and Haasenritter, J. and Hirsch, O. and Donner-Banzhoff, N. and Hüllermeier, Eyke}}, journal = {{Information Sciences}}, pages = {{16--29}}, title = {{{Reliable classification: Learning classifiers that distinguish aleatoric and epistemic uncertainty}}}, volume = {{255}}, year = {{2014}}, } @article{10312, author = {{Mernberger, M. and Moog, M. and Stork, S. and Zauner, S. and Maier, U.G. and Hüllermeier, Eyke}}, journal = {{J. Bioinformatics and Computational Biology}}, number = {{1}}, title = {{{Protein Sub-Cellular Localization Prediction for Special compartments via Optimized Time Series Distances}}}, volume = {{12}}, year = {{2014}}, } @article{10313, author = {{Calders, T. and Esposito, F. and Hüllermeier, Eyke and Meo, R.}}, journal = {{Machine Learning}}, number = {{1-2}}, pages = {{1--3}}, title = {{{Guest editors`introduction:special issue of the ECML/PKDD 2014 journal track}}}, volume = {{97}}, year = {{2014}}, } @article{10314, author = {{Busa-Fekete, Robert and Szörényi, B. and Weng, P. and Cheng, W. and Hüllermeier, Eyke}}, journal = {{Machine Learning}}, number = {{3}}, pages = {{327--351}}, title = {{{Preference-Based Reinforcement Learning: evolutionary direct policy search using a preference-based racing algorithm}}}, volume = {{97}}, year = {{2014}}, } @article{10315, author = {{Montanés, E. and Senge, Robin and Barranquero, J. and Quevedo, J.R. and Del Coz, J.J. and Hüllermeier, Eyke}}, journal = {{Pattern Recognition}}, number = {{3}}, pages = {{1494--1508}}, title = {{{Dependent binary relevance models for multi-label classification}}}, volume = {{47}}, year = {{2014}}, } @article{10316, author = {{Krempl, G. and Zliobaite, I. and Brzezinski, D. and Hüllermeier, Eyke and Last, M. and Lemaire, V. and Noack, T. and Shaker, Ammar and Sievi, S. and Spiliopoulou, M. and Stefanowski, J.}}, journal = {{SIGKDD Explorations}}, number = {{1}}, pages = {{1--10}}, title = {{{Open challenges for data stream mining research}}}, volume = {{16}}, year = {{2014}}, } @article{10317, author = {{Krotzky, T. and Fober, T. and Hüllermeier, Eyke and Klebe, G.}}, journal = {{IEEE/ACM Trans. Comput. Biology Bioinform.}}, number = {{5}}, pages = {{878--890}}, title = {{{Extended Graph-Based Models for Enhanced Similarity Search in Cavbase}}}, volume = {{11}}, year = {{2014}}, } @article{10318, author = {{Stock, M. and Fober, T. and Hüllermeier, Eyke and Glinca, S, and Klebe, G. and Pahikkala, T. and Airola, A. and De Baets, B. and Wageman, W.}}, journal = {{IEEE/ACM Trans. Comput. Biology Bioinform.}}, number = {{6}}, pages = {{1157--1169}}, title = {{{Identification of Functionally Releated Enzymes by Learning to Rank Methods}}}, volume = {{11}}, year = {{2014}}, } @article{1375, author = {{Beister, Frederic and Dräxler, Martin and Aelken, Jörg and Karl, Holger}}, issn = {{0140-3664}}, journal = {{Computer Communications}}, pages = {{77--85}}, publisher = {{Elsevier BV}}, title = {{{Power model design for ICT systems – A generic approach}}}, doi = {{10.1016/j.comcom.2014.02.007}}, volume = {{50}}, year = {{2014}}, } @inproceedings{24300, author = {{Wessel, Jan and Schmalz, Klaus and Cahill, Brian and Scheytt, Christoph}}, booktitle = {{Elektrotechnisches Kolloquium}}, title = {{{Design of an Electrical Interferometer at 120 GHz for Contactless Permittivity Characterization}}}, year = {{2014}}, } @inproceedings{24305, abstract = {{Energy efficiency drives the development of more and more complex low-power designs. Based on dynamic power management techniques, multiple voltage islands as well as a huge amount of power states are specified that have to be tested carefully. In this context, low-power design should start at an early stage using state-of-the-art system-level modeling and simulation techniques. However, there is neither a programming language nor any modeling standard that reflects variable power together with its functional side effects in a well-suited abstract manner. To overcome this limitation, we present a modeling approach on top of SystemC TLM to capture low-power design characteristics at electronic system-level. We demonstrate the usability by means of an existing open-source low-power design. The experimental results show that appropriate TLM instrumentation cause only minimal simulation overhead, but offer sufficient details to identify common low-power design errors.}}, author = {{Mischkalla, Fabian and Müller, Wolfgang}}, booktitle = {{Embedded Computer Systems: Architectures, Modeling, and Simulation (SAMOS XIV)}}, publisher = {{IEEE}}, title = {{{Architectural Low-Power Design Using Transaction-Based System Modeling and Simulation}}}, doi = {{10.1109/SAMOS.2014.6893219}}, year = {{2014}}, } @inproceedings{24308, abstract = {{A 115 GHz slow wave transmission line intended for phase detection based integrated biosensors is presented. The structure was fabricated in a 130 nm SiGe process. It achieved the targeted overall phase shift of 1° at 115 GHz. Moreover, the phase can be adjusted by 16 switches using Heterojunction Bipolar (HBT) transistors leading to a phase resolution of 0.125°. The change in input and output matching over all configurations of the switches is not higher than 0.8 dB and the transmission S 21 varies with less than 0.7 dB. To the authors knowledge, it is the first switchable slow wave structure using microstrip transmission lines along with a bipolar switch circuitry. Moreover, the presented structure provides a very powerful solution for real-time digital read-outs in integrated biosensors, without need of additional signal processing steps.}}, author = {{Wessel, Jan and Schmalz, Klaus and Scheytt, Christoph and Meliani, Chafik}}, booktitle = {{Microwave Symposium (IMS), 2014 IEEE MTT-S International}}, pages = {{1 -- 3}}, publisher = {{IEEE}}, title = {{{ Switchable slow wave transmission line in 130 nm SiGe technology at 115 GHz for phase detection based biosensors}}}, doi = {{10.1109/MWSYM.2014.6848446}}, year = {{2014}}, } @inproceedings{24303, abstract = {{A calibration technique as well as measurement results for a 7 GHz Biosensor are presented. It is shown that the applied sensor structure can be calibrated by adjusting the phase of a sensing element's transmission S21. This is realized by slowing down the wave traveling a microstrip line serving as a reference in the differential sensor structure. The dielectric properties along with certain physical boundaries of an obstacle covering parts of the microstrip line evoke that effect. Measurements with an ethanol serious along with simulation results showed that sensitivity can be increased substantially with this calibration technique. A change of the real part of the sample's permittivity of 48 leads to a 18 MHz frequency shift.}}, author = {{Wessel, Jan and Schmalz, Klaus and Scheytt, Christoph and Meliani, Chafik and Cahill, Brian}}, booktitle = {{European Microwave Conference (EuMC)}}, pages = {{699 -- 702}}, publisher = {{IEEE}}, title = {{{A 7 GHz biosensor for permittivity change with enhanced sensitivity through phase compensation}}}, doi = {{10.1109/EuMC.2014.6986530}}, volume = {{44th}}, year = {{2014}}, }