Beyond Sliding Windows: Object Localization by Efficient Subwindow Search, Christoph H. Lampert, Matthew B. Blaschko, Thomas Hofmann, IEEE Computer Vision and Pattern Recognition (CVPR), 2008.
Robust Collaborative Filtering, Bhaskar Mehta, Thomas Hofmann, Wolfgang Nejdl, ACM Conference on Recommender Systems, 2007, pp. 49-56.
A brain computer interface with online feedback based on magnetoencephalography, Thomas Navin Lal, Michael Schroeder, N. Jeremy Hill, Hubert Preiss, Thilo Hinterberger, J"rgen Mellinger, Martin Bogdan, Wolfgang Rosenstiel, Thomas Hofmann, Niels Birbaumer, Bernhard Schoelkopf, ICML, 2005, pp. 465-472.
Collaborative Machine Learning, Thomas Hofmann, Justin Basilico, From Integrated Publication and Information Systems to Virtual Information and Knowledge Environments, 2005, pp. 173-182.
From bits and bytes to information and knowledge, Thomas Hofmann, CIKM, 2005, pp. 3.
Non-redundant clustering with conditional ensembles, David Gondek, Thomas Hofmann, KDD, 2005, pp. 70-77.
A joint framework for collaborative and content filtering, Justin Basilico, Thomas Hofmann, SIGIR, 2004, pp. 550-551.
Gaussian process classification for segmenting and annotating sequences, Yasemin Altun, Thomas Hofmann, Alex J. Smola, ICML, 2004.
Hierarchical document categorization with support vector machines, Lijuan Cai, Thomas Hofmann, CIKM, 2004, pp. 78-87.
Latent semantic models for collaborative filtering, Thomas Hofmann, ACM Trans. Inf. Syst., vol. 22 (2004), pp. 89-115.
Learning Over Compact Metric Spaces, H. Quang Minh, Thomas Hofmann, COLT, 2004, pp. 239-254.
Non-Redundant Data Clustering, David Gondek, Thomas Hofmann, ICDM, 2004, pp. 75-82.
Semi-supervised Learning on Directed Graphs, Dengyong Zhou, Bernhard Sch"lkopf, Thomas Hofmann, NIPS, 2004.
Support vector machine learning for interdependent and structured output spaces, Ioannis Tsochantaridis, Thomas Hofmann, Thorsten Joachims, Yasemin Altun, ICML, 2004.
Unifying collaborative and content-based filtering, Justin Basilico, Thomas Hofmann, ICML, 2004.
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, 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, SIGIR Forum, vol. 37 (2003), pp. 31-47.
Collaborative filtering via gaussian probabilistic latent semantic analysis, Thomas Hofmann, SIGIR, 2003, pp. 259-266.
Hidden Markov Support Vector Machines, Yasemin Altun, Ioannis Tsochantaridis, Thomas Hofmann, ICML, 2003, pp. 3-10.
Hierarchical Semantic Classification: Word Sense Disambiguation with World Knowledge, Massimiliano Ciaramita, Thomas Hofmann, Mark Johnson, IJCAI, 2003, pp. 817-822.
Multiple-Instance Learning via Disjunctive Programming Boosting, Stuart Andrews, Thomas Hofmann, NIPS, 2003.
Text categorization by boosting automatically extracted concepts, Lijuan Cai, Thomas Hofmann, SIGIR, 2003, pp. 182-189.
Discriminative Learning for Label Sequences via Boosting, Yasemin Altun, Thomas Hofmann, Mark Johnson, NIPS, 2002, pp. 977-984.
Multiple Instance Learning with Generalized Support Vector Machines, Stuart Andrews, Thomas Hofmann, Ioannis Tsochantaridis, AAAI/IAAI, 2002, pp. 943-944.
Predicting CNS Permeability of Drug Molecules: Comparison of Neural Network and Support Vector Machine Algorithms, Scott Doniger, Thomas Hofmann, Miao-Hui Joanne Yeh, Journal of Computational Biology, vol. 9 (2002), pp. 849.
Support Vector Machines for Multiple-Instance Learning, Stuart Andrews, Ioannis Tsochantaridis, Thomas Hofmann, NIPS, 2002, pp. 561-568.
Support Vector Machines for Polycategorical Classification, Ioannis Tsochantaridis, Thomas Hofmann, ECML, 2002, pp. 456-467.
Learning What People (Don't) Want, Thomas Hofmann, ECML, 2001, pp. 214-225.
Text Classification in a Hierarchical Mixture Model for Small Training Sets, Kristina Toutanova, Francine Chen, Kris Popat, Thomas Hofmann, CIKM, 2001, pp. 105-112.
Unsupervised Learning by Probabilistic Latent Semantic Analysis, Thomas Hofmann, Machine Learning, vol. 42 (2001), pp. 177-196.
A theory of proximity based clustering: structure detection by optimization, Jan Puzicha, Thomas Hofmann, Joachim M. Buhmann, Pattern Recognition, vol. 33 (2000), pp. 617-634.
Learning Curved Multinomial Subfamilies for Natural Language Processing and Information Retrieval, Keith B. Hall, Thomas Hofmann, ICML, 2000, pp. 351-358.
Learning probabilistic models of the Web, Thomas Hofmann, SIGIR, 2000, pp. 369-371.
ProbMap - A probabilistic approach for mapping large document collections, Thomas Hofmann, Intell. Data Anal., vol. 4 (2000), pp. 149-164.
The Missing Link - A Probabilistic Model of Document Content and Hypertext Connectivity, David A. Cohn, Thomas Hofmann, NIPS, 2000, pp. 430-436.
Histogram Clustering for Unsupervised Image Segmentation, Jan Puzicha, Joachim M. Buhmann, Thomas Hofmann, CVPR, 1999, pp. 2602-2608.
Histogram clustering for unsupervised segmentation and image retrieval, Jan Puzicha, Thomas Hofmann, Joachim M. Buhmann, Pattern Recognition Letters, vol. 20 (1999), pp. 899-909.
Latent Class Models for Collaborative Filtering, Thomas Hofmann, Jan Puzicha, IJCAI, 1999, pp. 688-693.
Learning the Similarity of Documents: An Information-Geometric Approach to Document Retrieval and Categorization, Thomas Hofmann, NIPS, 1999, pp. 914-920.
Probabilistic Latent Semantic Analysis, Thomas Hofmann, UAI, 1999, pp. 289-296.
Probabilistic Latent Semantic Indexing, Thomas Hofmann, SIGIR, 1999, pp. 50-57.
Probabilistic Topic Maps: Navigating through Large Text Collections, Thomas Hofmann, IDA, 1999, pp. 161-172.
The Cluster-Abstraction Model: Unsupervised Learning of Topic Hierarchies from Text Data, Thomas Hofmann, IJCAI, 1999, pp. 682-687.
Discrete Mixture Models for Unsupervised Image Segmentation, Jan Puzicha, Joachim M. Buhmann, Thomas Hofmann, DAGM-Symposium, 1998, pp. 135-142.
Learning from Dyadic Data, Thomas Hofmann, Jan Puzicha, Michael I. Jordan, NIPS, 1998, pp. 466-472.
Unsupervised Texture Segmentation in a Deterministic Annealing Framework, Thomas Hofmann, Jan Puzicha, Joachim M. Buhmann, IEEE Trans. Pattern Anal. Mach. Intell., vol. 20 (1998), pp. 803-818.
Active Data Clustering, Thomas Hofmann, Joachim M. Buhmann, NIPS, 1997.
An Optimization Approach to Unsupervised Hierarchical Texture Segmentation, Thomas Hofmann, Jan Puzicha, Joachim M. Buhmann, ICIP (3), 1997, pp. 213-216.
Correction to "Pairwise Data Clustering by Deterministic Annealing", Thomas Hofmann, Joachim M. Buhmann, IEEE Trans. Pattern Anal. Mach. Intell., vol. 19 (1997), pp. 192.
Deterministic Annealing for Unsupervised Texture Segmentation, Thomas Hofmann, Jan Puzicha, Joachim M. Buhmann, EMMCVPR, 1997, pp. 213-228.
Non-parametric Similarity Measures for Unsupervised Texture Segmentation and Image Retrieval, Jan Puzicha, Thomas Hofmann, Joachim M. Buhmann, CVPR, 1997, pp. 267-272.
Pairwise Data Clustering by Deterministic Annealing, Thomas Hofmann, Joachim M. Buhmann, IEEE Trans. Pattern Anal. Mach. Intell., vol. 19 (1997), pp. 1-14.
An Annealed ``Neural Gas'' Network for Robust Vector Quantization, Thomas Hofmann, Joachim M. Buhmann, ICANN, 1996, pp. 151-156.
Inferring Hierarchical Clustering Structures by Deterministic Annealing, Thomas Hofmann, Joachim M. Buhmann, KDD, 1996, pp. 363-366.
The Mobile Robot RHINO, Joachim M. Buhmann, Wolfram Burgard, Armin B. Cremers, Dieter Fox, Thomas Hofmann, Frank E. Schneider, Jiannis Strikos, Sebastian Thrun, AI Magazine, vol. 16 (1995), pp. 31-38.
Multidimensional Scaling and Data Clustering, Thomas Hofmann, Joachim M. Buhmann, NIPS, 1994, pp. 459-466.
Central and Pairwise Data Clustering by Competitive Neural Networks, Joachim M. Buhmann, Thomas Hofmann, NIPS, 1993, pp. 104-111.