Gerhard Widmer Papers on DiSC'04 Visualizing changes in the structure of data for exploratory feature selection Visualizing Changes in the Structure of Data for Exploratory Feature Extraction Publications Note: Links lead to the DBLP on the Web. Gerhard Widmer Asmir Tobudic , Gerhard Widmer: Playing Mozart Phrase by Phrase. ICCBR 2003 : 552-566 Asmir Tobudic , Gerhard Widmer: Relational IBL in Music with a New Structural Similarity Measure. ILP 2003 : 365-382 Elias Pampalk , Werner Goebl , Gerhard Widmer: Visualizing changes in the structure of data for exploratory feature selection. KDD 2003 : 157-166 Gerhard Widmer: Discovering simple rules in complex data: A meta-learning algorithm and some surprising musical discoveries. Artif. Intell. 146 (2): 129-148 (2003) Gerhard Widmer: In Search of the Horowitz Factor: Interim Report on a Musical Discovery Project. ALT 2002 : 41 Gerhard Widmer: In Search of the Horowitz Factor: Interim Report on a Musical Discovery Project. Discovery Science 2002 : 13-21 Efstathios Stamatatos , Gerhard Widmer: Music Performer Recognition Using an Ensemble of Simple Classifiers. ECAI 2002 : 335-339 Marcus-Christopher Ludl , Gerhard Widmer: Towards a Simple Clustering Criterion Based on Minimum Length Encoding. ECML 2002 : 258-269 Simon Dixon , Werner Goebl , Gerhard Widmer: Real Time Tracking and Visualisation of Musical Expression. ICMAI 2002 : 58-68 Björn Bringmann , Stefan Kramer , Friedrich Neubarth , Hannes Pirker , Gerhard Widmer: Transformation-Based Regression. ICML 2002 : 59-66 Gerhard Widmer: Discovering Strong Principles of Expressive Music Performance with the PLCG Rule Learning Strategy. ECML 2001 : 552-563 Gerhard Widmer: The Musical Expression Project: A Challenge for Machine Learning and Knowledge Discovery. ECML 2001 : 603-614 Alexander K. Seewald , Johann Petrak , Gerhard Widmer: Hybrid Decision Tree Learners with Alternative Leaf Classifiers: An Empirical Study. FLAIRS Conference 2001 : 407-411 Gerhard Widmer: The Musical Expression Project: A Challenge for Machine Learning and Knowledge Discovery. PKDD 2001 : 495-506 Gerhard Widmer: Using AI and machine learning to study expressive music performance: project survey and first report. AI Commun. 14 (3): 149-162 (2001) Stefan Kramer , Gerhard Widmer, Bernhard Pfahringer , Michael de Groeve : Prediction of Ordinal Classes Using Regression Trees. Fundam. Inform. 47 (1-2): 1-13 (2001) Marcus-Christopher Ludl , Gerhard Widmer: Relative Unsupervised Discretization for Regresseion Problems. ECML 2000 : 246-253 Stefan Kramer , Gerhard Widmer, Bernhard Pfahringer , Michael de Groeve : Prediction of Ordinal Classes Using Regression Trees. ISMIS 2000 : 426-434 Marcus-Christopher Ludl , Gerhard Widmer: Relative Unsupervised Discretization for Association Rule Mining. PKDD 2000 : 148-158 Gerhard Widmer: On the Potential of Machine Learning for Music Research. Readings in Music and Artificial Intelligence 2000 : 69-84 Gerhard Widmer, Miroslav Kubat : Guest Editors' Introduction. Machine Learning 32 (2): 83-84 (1998) Maarten van Someren , Gerhard Widmer: Machine Learning: ECML-97, 9th European Conference on Machine Learning, Prague, Czech Republic, April 23-25, 1997, Proceedings Springer 1997 Gerhard Widmer: Tracking Context Changes through Meta-Learning. Machine Learning 27 (3): 259-286 (1997) Gerhard Widmer: What Is It That Makes It a Horowitz? Empirical Musicology via Machine Learning. ECAI 1996 : 458-462 Gerhard Widmer: Recognition and Exploitation of Contextual CLues via Incremental Meta-Learning. ICML 1996 : 525-533 Gerhard Widmer, Miroslav Kubat : Learning in the Presence of Concept Drift and Hidden Contexts. Machine Learning 23 (1): 69-101 (1996) Miroslav Kubat , Gerhard Widmer: Adapting to Drift in Continuous Domains (Extended Abstract). ECML 1995 : 307-310 Gerhard Widmer: The Synergy of Music Theory and Al: Learning Multi-Level Expressive Interpretation. AAAI 1994 : 114-119 Gerhard Widmer: Combining Robustness and Flexibility in Learning Drifting Concepts. ECAI 1994 : 468-472 Johannes Fürnkranz , Gerhard Widmer: Incremental Reduced Error Pruning. ICML 1994 : 70-77 Gerhard Widmer, Miroslav Kubat : Effective Learning in Dynamic Environments by Explicit Context Tracking. ECML 1993 : 227-243 Gerhard Widmer, Werner Horn , Bernhard Nagele : Automatic knowledge base refinement: learning from examples and deep knowledge in rheumatology. Artificial Intelligence in Medicine 5 (3): 225-243 (1993) Gerhard Widmer: Combining Knowledge-Based and Instance-Based Learning to Exploit Qualitative Knowledge. Informatica (Slovenia) 17 (4): (1993) Gerhard Widmer, Miroslav Kubat : Learning Flexible Concepts from Streams of Examples: FLORA 2. ECAI 1992 : 463-467 Bernhard Nagele , Gerhard Widmer, Werner Horn : Automatische Verfeinerung der Wissensbasis durch maschinelles Lernen in einem medizinischen Expertensystem. ÖGAI 1991 : 68-77 Gerhard Widmer: Using Plausible Explanations to Bias Empirical Generalizations in Weak Theory Domains. EWSL 1991 : 33-43 Gerhard Widmer: Wissensbasiertes Lernen in der Musik: Die Integration induktiver und deduktiver Lernmethoden. ÖGAI 1989 : 154-163 Gerhard Widmer: A Tight Integration of Deductive Learning. ML 1989 : 11-13 Gerhard Widmer, Werner Horn : VIE-PCX - Ein Expert System Shell für den PC. ÖGAI 1985 : 34-41 1 [ 30 ] 2 [ 31 ] 3 [ 10 ] 4 [ 31 ] [ 37 ] 5 [ 22 ] [ 24 ] 6 [ 1 ] [ 5 ] [ 8 ] 7 [ 22 ] [ 24 ] [ 30 ] 8 [ 6 ] [ 9 ] [ 13 ] [ 14 ] [ 19 ] 9 [ 21 ] [ 23 ] [ 32 ] 10 [ 5 ] [ 8 ] 11 [ 30 ] 12 [ 37 ] 13 [ 27 ] 14 [ 22 ] [ 24 ] 15 [ 30 ] 16 [ 27 ] 17 [ 18 ] 18 [ 33 ] 19 [ 38 ] [ 39 ] ©2004 Association for Computing Machinery