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Note: Links lead to the DBLP on the Web. Thomas Hofmann Thomas Hofmann: From bits and bytes to information and knowledge. CIKM 2005 : 3 Thomas Hofmann, Justin Basilico : Collaborative Machine Learning. From Integrated Publication and Information Systems to Virtual Information and Knowledge Environments 2005 : 173-182 Thomas Navin Lal , Michael Schröder , N. Jeremy Hill , Hubert Preißl , Thilo Hinterberger , Jürgen Mellinger , Martin Bogdan , Wolfgang Rosenstiel , Thomas Hofmann, Niels Birbaumer , Bernhard Schölkopf : A brain computer interface with online feedback based on magnetoencephalography. ICML 2005 : 465-472 David Gondek , Thomas Hofmann: Non-redundant clustering with conditional ensembles. KDD 2005 : 70-77 Lijuan Cai , Thomas Hofmann: Hierarchical document categorization with support vector machines. CIKM 2004 : 78-87 H. Quang Minh , Thomas Hofmann: Learning Over Compact Metric Spaces. COLT 2004 : 239-254 David Gondek , Thomas Hofmann: Non-Redundant Data Clustering. ICDM 2004 : 75-82 Yasemin Altun , Thomas Hofmann, Alex J. Smola : Gaussian process classification for segmenting and annotating sequences. ICML 2004 Ioannis Tsochantaridis , Thomas Hofmann, Thorsten Joachims , Yasemin Altun : Support vector machine learning for interdependent and structured output spaces. ICML 2004 Justin Basilico , Thomas Hofmann: Unifying collaborative and content-based filtering. ICML 2004 Dengyong Zhou , Bernhard Schölkopf , Thomas Hofmann: Semi-supervised Learning on Directed Graphs. NIPS 2004 Justin Basilico , Thomas Hofmann: A joint framework for collaborative and content filtering. SIGIR 2004 : 550-551 Thomas Hofmann: Latent semantic models for collaborative filtering. ACM Trans. Inf. Syst. 22 (1): 89-115 (2004) Yasemin Altun , Ioannis Tsochantaridis , Thomas Hofmann: Hidden Markov Support Vector Machines. ICML 2003 : 3-10 Massimiliano Ciaramita , Thomas Hofmann, Mark Johnson : Hierarchical Semantic Classification: Word Sense Disambiguation with World Knowledge. IJCAI 2003 : 817-822 Stuart Andrews , Thomas Hofmann: Multiple-Instance Learning via Disjunctive Programming Boosting. NIPS 2003 Lijuan Cai , Thomas Hofmann: Text categorization by boosting automatically extracted concepts. SIGIR 2003 : 182-189 Thomas Hofmann: Collaborative filtering via gaussian probabilistic latent semantic analysis. SIGIR 2003 : 259-266 James Allan , Jay Aslam , Nicholas J. Belkin , Chris Buckley , James P. Callan , W. Bruce Croft , Susan T. Dumais , Norbert Fuhr , Donna Harman , David J. Harper , Djoerd Hiemstra , Thomas Hofmann, Eduard H. Hovy , Wessel Kraaij , John D. Lafferty , Victor Lavrenko , David D. Lewis , Liz Liddy , R. Manmatha , Andrew McCallum , Jay M. Ponte , John M. Prager , Dragomir R. Radev , Philip Resnik , Stephen E. Robertson , Ronald Rosenfeld , Salim Roukos , Mark Sanderson , Rich Schwartz , Amit Singhal , Alan F. Smeaton , Howard R. Turtle , Ellen M. Voorhees , Ralph M. Weischedel , Jinxi Xu , ChengXiang Zhai : Challenges in information retrieval and language modeling: report of a workshop held at the center for intelligent information retrieval, University of Massachusetts Amherst, September 2002. SIGIR Forum 37 (1): 31-47 (2003) Stuart Andrews , Thomas Hofmann, Ioannis Tsochantaridis : Multiple Instance Learning with Generalized Support Vector Machines. AAAI/IAAI 2002 : 943-944 Ioannis Tsochantaridis , Thomas Hofmann: Support Vector Machines for Polycategorical Classification. ECML 2002 : 456-467 Stuart Andrews , Ioannis Tsochantaridis , Thomas Hofmann: Support Vector Machines for Multiple-Instance Learning. NIPS 2002 : 561-568 Yasemin Altun , Thomas Hofmann, Mark Johnson : Discriminative Learning for Label Sequences via Boosting. NIPS 2002 : 977-984 Scott Doniger , Thomas Hofmann, Miao-Hui Joanne Yeh : Predicting CNS Permeability of Drug Molecules: Comparison of Neural Network and Support Vector Machine Algorithms. Journal of Computational Biology 9 (6): 849 (2002) Kristina Toutanova , Francine Chen , Kris Popat , Thomas Hofmann: Text Classification in a Hierarchical Mixture Model for Small Training Sets. CIKM 2001 : 105-112 Thomas Hofmann: Learning What People (Don't) Want. ECML 2001 : 214-225 Thomas Hofmann: Unsupervised Learning by Probabilistic Latent Semantic Analysis. Machine Learning 42 (1/2): 177-196 (2001) Stéphane Ducasse , Thomas Hofmann, Oscar Nierstrasz : OpenSpaces: An Object-Oriented Framework for Reconfigurable Coordination Spaces. COORDINATION 2000 : 1-18 Keith Hall , Thomas Hofmann: Learning Curved Multinomial Subfamilies for Natural Language Processing and Information Retrieval. ICML 2000 : 351-358 David A. Cohn , Thomas Hofmann: The Missing Link - A Probabilistic Model of Document Content and Hypertext Connectivity. NIPS 2000 : 430-436 Thomas Hofmann: Learning probabilistic models of the Web. SIGIR 2000 : 369-371 Thomas Hofmann: ProbMap - A probabilistic approach for mapping large document collections. Intell. Data Anal. 4 (2): 149-164 (2000) Jan Puzicha , Thomas Hofmann, Joachim M. Buhmann : A theory of proximity based clustering: structure detection by optimization. Pattern Recognition 33 (4): 617-634 (2000) Jan Puzicha , Joachim M. Buhmann , Thomas Hofmann: Histogram Clustering for Unsupervised Image Segmentation. CVPR 1999 : 2602-2608 Thomas Hofmann: Probabilistic Topic Maps: Navigating through Large Text Collections. IDA 1999 : 161-172 Thomas Hofmann: The Cluster-Abstraction Model: Unsupervised Learning of Topic Hierarchies from Text Data. IJCAI 1999 : 682-687 Thomas Hofmann, Jan Puzicha : Latent Class Models for Collaborative Filtering. IJCAI 1999 : 688-693 Thomas Hofmann: Learning the Similarity of Documents: An Information-Geometric Approach to Document Retrieval and Categorization. NIPS 1999 : 914-920 Thomas Hofmann: Probabilistic Latent Semantic Indexing. SIGIR 1999 : 50-57 Thomas Hofmann: Probabilistic Latent Semantic Analysis. UAI 1999 : 289-296 Jan Puzicha , Thomas Hofmann, Joachim M. Buhmann : Histogram clustering for unsupervised segmentation and image retrieval. Pattern Recognition Letters 20 (9): 899-909 (1999) Jan Puzicha , Joachim M. Buhmann , Thomas Hofmann: Discrete Mixture Models for Unsupervised Image Segmentation. DAGM-Symposium 1998 : 135-142 Thomas Hofmann, Jan Puzicha , Michael I. Jordan : Learning from Dyadic Data. NIPS 1998 : 466-472 Thomas Hofmann, Jan Puzicha , Joachim M. Buhmann : Unsupervised Texture Segmentation in a Deterministic Annealing Framework. IEEE Trans. Pattern Anal. Mach. Intell. 20 (8): 803-818 (1998) Jan Puzicha , Thomas Hofmann, Joachim M. Buhmann : Non-parametric Similarity Measures for Unsupervised Texture Segmentation and Image Retrieval. CVPR 1997 : 267-272 Thomas Hofmann, Jan Puzicha , Joachim M. Buhmann : Deterministic Annealing for Unsupervised Texture Segmentation. EMMCVPR 1997 : 213-228 Thomas Hofmann, Jan Puzicha , Joachim M. Buhmann : An Optimization Approach to Unsupervised Hierarchical Texture Segmentation. ICIP (3) 1997 : 213-216 Thomas Hofmann, Joachim M. Buhmann : Active Data Clustering. NIPS 1997 Thomas Hofmann, Joachim M. Buhmann : Pairwise Data Clustering by Deterministic Annealing. IEEE Trans. Pattern Anal. Mach. Intell. 19 (1): 1-14 (1997) Thomas Hofmann, Joachim M. Buhmann : Correction to "Pairwise Data Clustering by Deterministic Annealing". IEEE Trans. Pattern Anal. Mach. Intell. 19 (2): 192 (1997) Thomas Hofmann, Joachim M. Buhmann : An Annealed ``Neural Gas'' Network for Robust Vector Quantization. ICANN 1996 : 151-156 Thomas Hofmann, Joachim M. Buhmann : Inferring Hierarchical Clustering Structures by Deterministic Annealing. KDD 1996 : 363-366 Joachim M. Buhmann , Wolfram Burgard , Armin B. Cremers , Dieter Fox , Thomas Hofmann, Frank E. Schneider , Jiannis Strikos , Sebastian Thrun : The Mobile Robot RHINO. AI Magazine 16 (2): 31-38 (1995) Thomas Hofmann, Joachim M. Buhmann : Multidimensional Scaling and Data Clustering. NIPS 1994 : 459-466 Joachim M. Buhmann , Thomas Hofmann: Central and Pairwise Data Clustering by Competitive Neural Networks. NIPS 1993 : 104-111 1 [ 37 ] 2 [ 33 ] [ 42 ] [ 47 ] [ 48 ] 3 [ 34 ] [ 36 ] [ 40 ] 4 [ 37 ] 5 [ 44 ] [ 46 ] [ 54 ] 6 [ 37 ] 7 [ 53 ] 8 [ 53 ] 9 [ 37 ] 10 [ 1 ] [ 2 ] [ 3 ] [ 4 ] [ 5 ] [ 6 ] [ 7 ] [ 8 ] [ 9 ] [ 10 ] [ 11 ] [ 12 ] [ 14 ] [ 15 ] [ 22 ] [ 23 ] 11 [ 3 ] 12 [ 39 ] [ 51 ] 13 [ 37 ] 14 [ 31 ] 15 [ 41 ] 16 [ 26 ] 17 [ 3 ] 18 [ 37 ] 19 [ 32 ] 20 [ 28 ] 21 [ 37 ] 22 [ 3 ] 23 [ 37 ] 24 [ 49 ] [ 52 ] 25 [ 27 ] 26 [ 37 ] 27 [ 37 ] 28 [ 37 ] 29 [ 53 ] 30 [ 53 ] 31 [ 37 ] 32 [ 47 ] 33 [ 33 ] [ 41 ] 34 [ 13 ] 35 [ 37 ] 36 [ 37 ] 37 [ 53 ] 38 [ 37 ] 39 [ 37 ] 40 [ 37 ] 41 [ 37 ] 42 [ 37 ] 43 [ 53 ] 44 [ 50 ] 45 [ 28 ] 46 [ 37 ] 47 [ 31 ] 48 [ 37 ] 49 [ 53 ] 50 [ 9 ] [ 10 ] [ 11 ] [ 12 ] [ 13 ] [ 14 ] [ 15 ] [ 19 ] [ 22 ] [ 23 ] 51 [ 37 ] 52 [ 37 ] 53 [ 37 ] 54 [ 37 ] 55 [ 53 ] 56 [ 37 ] 57 [ 37 ] 58 [ 3 ] 59 [ 45 ] [ 53 ] 60 [ 53 ] 61 [ 37 ] 62 [ 37 ] 63 [ 37 ] 64 [ 48 ] 65 [ 3 ] 66 [ 3 ] 67 [ 31 ] 68 [ 34 ] [ 35 ] [ 36 ] [ 42 ] [ 47 ] 69 [ 37 ] 70 [ 37 ] 71 [ 37 ] 72 [ 37 ] 73 [ 32 ] 74 [ 37 ] 75 [ 45 ]  ©2006 Association for Computing Machinery |
