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Note: Links lead to the DBLP on the Web. Volker Tresp Kai Yu , Volker Tresp: Heterogenous Data Fusion via a Probabilistic Latent-Variable Model. ARCS 2004 : 20-30 Kai Yu , Volker Tresp, Shipeng Yu : A nonparametric hierarchical bayesian framework for information filtering. SIGIR 2004 : 353-360 Kai Yu , Anton Schwaighofer , Volker Tresp, Xiaowei Xu , Hans-Peter Kriegel : Probabilistic Memory-Based Collaborative Filtering. IEEE Trans. Knowl. Data Eng. 16 (1): 56-69 (2004) Michael Haft , Reimar Hofmann , Volker Tresp: Generative binary codes. Pattern Anal. Appl. 6 (4): 269-284 (2004) Kai Yu , Wei-Ying Ma , Volker Tresp, Zhao Xu , Xiaofei He , HongJiang Zhang , Hans-Peter Kriegel : Knowing a tree from the forest: art image retrieval using a society of profiles. ACM Multimedia 2003 : 622-631 Zhao Xu , Xiaowei Xu , Kai Yu , Volker Tresp: A Hybrid Relevance-Feedback Approach to Text Retrieval. ECIR 2003 : 281-293 Zhao Xu , Kai Yu , Volker Tresp, Xiaowei Xu , Jizhi Wang : Representative Sampling for Text Classification Using Support Vector Machines. ECIR 2003 : 393-407 Volker Tresp, Kai Yu : An Introduction to Nonparametric Hierarchical Bayesian Modelling with a Focus on Multi-agent Learning. European Summer School on Multi-AgentControl 2003 : 290-312 Anton Schwaighofer , Marian Grigoras , Volker Tresp, Clemens Hoffmann : GPPS: A Gaussian Process Positioning System for Cellular Networks. NIPS 2003 Kai Yu , Anton Schwaighofer , Volker Tresp, Wei-Ying Ma , HongJiang Zhang : Collaborative Ensemble Learning: Combining Collaborative and Content-Based Information Filtering via Hierarchical Bayes. UAI 2003 : 616-623 Kai Yu , Xiaowei Xu , Anton Schwaighofer , Volker Tresp, Hans-Peter Kriegel : Removing redundancy and inconsistency in memory-based collaborative filtering. CIKM 2002 : 52-59 Anton Schwaighofer , Volker Tresp, Peter Mayer , Alexander K. Scheel , Gerhard Müller : The RA Scanner: Prediction of Rheumatoid Joint Inflammation Based on Laser Imaging. NIPS 2002 : 1409-1416 Anton Schwaighofer , Volker Tresp: Transductive and Inductive Methods for Approximate Gaussian Process Regression. NIPS 2002 : 953-960 Todd K. Leen , Thomas G. Dietterich , Volker Tresp: Advances in Neural Information Processing Systems 13, Papers from Neural Information Processing Systems (NIPS) 2000, Denver, CO, USA MIT Press 2001 Joachim Horn , Thomas Birkhölzer , Oliver Hogl , Marco Pellegrino , Ruxandra Scheiterer , Kai-Uwe Schmidt , Volker Tresp: Knowledge Acquisition and Automated Generation of Bayesian Networks for a Medical Dialogue and Advisory System. AIME 2001 : 199-202 Volker Tresp, Anton Schwaighofer : Scalable Kernel Systems. ICANN 2001 : 285-291 Anton Schwaighofer , Volker Tresp: The Bayesian Committee Support Vector Machine. ICANN 2001 : 411-420 Volker Tresp: Scaling Kernel-Based Systems to Large Data Sets. Data Min. Knowl. Discov. 5 (3): 197-211 (2001) Volker Tresp: The generalized Bayesian committee machine. KDD 2000 : 130-139 Volker Tresp: Mixtures of Gaussian Processes. NIPS 2000 : 654-660 Volker Tresp: A Bayesian Committee Machine. Neural Computation 12 (11): 2719-2741 (2000) Thomas Briegel , Volker Tresp: Robust Neural Network Regression for Offline and Online Learning. NIPS 1999 : 407-413 Volker Tresp, Michael Haft , Reimar Hofmann : Mixture Approximations to Bayesian Networks. UAI 1999 : 639-646 Thomas Briegel , Volker Tresp: Fisher Scoring and a Mixture of Modes Approach for Approximate Inference and Learning in Nonlinear State Space Models. NIPS 1998 : 403-409 Jaakko Hollmén , Volker Tresp: Call-Based Fraud Detection in Mobile Communication Networks Using a Hierarchical Regime-Switching Model. NIPS 1998 : 889-895 Volker Tresp, Reimar Hofmann : Nonlinear Time-Series Prediction with Missing and Noisy Data. Neural Computation 10 (3): 731-747 (1998) Michiaki Taniguchi , Volker Tresp: Combining Regularized Neural Networks. ICANN 1997 : 349-354 Volker Tresp, Thomas Briegel : A Solution for Missing Data in Recurrent Neural Networks with an Application to Blood Glucose Prediction. NIPS 1997 Reimar Hofmann , Volker Tresp: Nonlinear Markov Networks for Continuous Variables. NIPS 1997 Volker Tresp, Jürgen Hollatz , Subutai Ahmad : Representing Probabilistic Rules with Networks of Gaussian Basis Functions. Machine Learning 27 (2): 173-200 (1997) Michiaki Taniguchi , Volker Tresp: Averaging Regularized Estimators. Neural Computation 9 (5): 1163-1178 (1997) Volker Tresp, Ralph Neuneier , Hans-Georg Zimmermann : Early Brain Damage. NIPS 1996 : 669-675 Reimar Hofmann , Volker Tresp: Discovering Structure in Continuous Variables Using Bayesian Networks. NIPS 1995 : 500-506 Dirk Ormoneit , Volker Tresp: Improved Gaussian Mixture Density Estimates Using Bayesian Penalty Terms and Network Averaging. NIPS 1995 : 542-548 Volker Tresp: Die besonderen Eigenschaften Neuronaler Netze bei der Approximation von Funktionen. KI 9 (5): 12-17 (1995) Volker Tresp, Michiaki Taniguchi : Combining Estimators Using Non-Constant Weighting Functions. NIPS 1994 : 419-426 Volker Tresp, Ralph Neuneier , Subutai Ahmad : Efficient Methods for Dealing with Missing Data in Supervised Learning. NIPS 1994 : 689-696 Volker Tresp, Subutai Ahmad , Ralph Neuneier : Training Neural Networks with Deficient Data. NIPS 1993 : 128-135 Martin F. Schlang , Volker Tresp, Klaus Abraham-Fuchs , Wolfgang Härer , P. Weismüller : Neuronale Netze zur Segmentierung und Clusterung von biomagnetischen Signalen. DAGM-Symposium 1992 : 180-185 Jürgen Hollatz , Volker Tresp: Integrating Rule-Based Knowledge into Neural Computing. DAGM-Symposium 1992 : 88-95 Subutai Ahmad , Volker Tresp: Some Solutions to the Missing Feature Problem in Vision. NIPS 1992 : 393-400 Volker Tresp, Jürgen Hollatz , Subutai Ahmad : Network Structuring and Training Using Rule-Based Knowledge. NIPS 1992 : 871-878 Volker Tresp: A Neural Architecture for 2D and 3D Vision. DAGM-Symposium 1991 : 437-445 Martin Röscheisen , Reimar Hofmann , Volker Tresp: Neural Control for Rolling Mills: Incorporating Domain Theories to Overcome Data Deficiency. NIPS 1991 : 659-666 Volker Tresp: A Neural Network Approach for Three-Dimensional Object Recognition. NIPS 1990 : 306-312 1 [ 7 ] 2 [ 4 ] [ 5 ] [ 8 ] [ 9 ] [ 16 ] 3 [ 31 ] 4 [ 18 ] [ 22 ] [ 24 ] 5 [ 32 ] 6 [ 37 ] 7 [ 23 ] [ 42 ] 8 [ 7 ] 9 [ 41 ] 10 [ 37 ] 11 [ 2 ] [ 13 ] [ 17 ] [ 20 ] [ 23 ] [ 42 ] 12 [ 31 ] 13 [ 4 ] [ 6 ] [ 16 ] 14 [ 21 ] 15 [ 31 ] 16 [ 35 ] [ 41 ] [ 43 ] 17 [ 32 ] 18 [ 36 ] [ 41 ] 19 [ 34 ] 20 [ 34 ] 21 [ 8 ] [ 9 ] [ 14 ] 22 [ 12 ] 23 [ 31 ] 24 [ 2 ] 25 [ 34 ] 26 [ 31 ] 27 [ 7 ] 28 [ 31 ] 29 [ 29 ] [ 30 ] [ 33 ] [ 34 ] [ 35 ] [ 36 ] [ 37 ] [ 43 ] 30 [ 10 ] [ 15 ] [ 19 ] 31 [ 39 ] 32 [ 7 ] 33 [ 35 ] [ 39 ] [ 40 ] [ 43 ] 34 [ 39 ] [ 40 ] [ 41 ] 35 [ 35 ] [ 36 ] [ 38 ] [ 39 ] [ 40 ] [ 41 ] [ 43 ] [ 44 ] [ 45 ] 36 [ 44 ] 37 [ 36 ] [ 41 ] 38 [ 14 ]  ©2005 Association for Computing Machinery |
