Year/
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CV |
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CB |
Reference |
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| 2004 |
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Hierarchical Document Categorization with Support Vector Machines
Lijuan Cai, Thomas Hofmann
ACM 13th Conference on Information and Knowledge Management, 2004 |
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Semi-supervised Learning on Directed Graphs
Dengyong Zhou, Bernhard Schoelkopf, Thomas Hofmann
Advances in Neural Information Processing Systems (NIPS), 2004 |
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Non-Redundant Data Clustering
David Gondek, Thomas Hofmann
4th IEEE International Conference on Data Mining, 2004 (best paper award) |
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Exponential Families for Conditional Random Fields
Yasemin Altun, Alex Smola, and Thomas Hofmann
20th Conference on Uncertainty in Artificial Intelligence (UAI), 2004 |
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Learning over Compact Metric Spaces
Ha Quang Minh, Thomas Hofmann
17th Annual Conference on Learning Theory (COLT), 2004 |
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A Joint Framework for Collaborative and Content Filtering
Justin Basilico and Thomas Hofmann
27th Annual International ACM SIGIR Conference, 2004 |
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Support Vector Machine Learning for Interdependent and Structured Output Spaces
Ioannis Tsochantaridis, Thomas Hofmann, Thorsten Joachims, Yasemin Altun
International Conference on Machine Learning (ICML), 2004 |
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Unifying Collaborative and Content-Based Filtering
Justin Basilico and Thomas Hofmann
International Conference on Machine Learning (ICML), 2004 |
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Gaussian Process Classification for Segmenting and Annotating Sequences
Yasemin Altun, Thomas Hofmann, and Alex Smola
International Conference on Machine Learning (ICML), 2004 |
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Latent Semantic Models for Collaborative Filtering
Thomas Hofmann
ACM Transactions on Information Systems, 2004, Vol 22(1), pp. 89-115 |
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| 2003 |
| W |
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Learning with Taxonomies: Classifying Documents and Words
Thomas Hofmann, Lijuan Cai, and Massimiliano Ciaramita
Workshop on Syntax, Semantics, and Statistics, Neural Information Processing (NIPS), 2003 |
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Conditional Information Bottleneck Clustering
David Gondek and Thomas Hofmann
3rd IEEE International Conference on Data Mining, Workshop on Clustering Large Data Sets, 2003.
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Multiple Instance Learning via Disjunctive Programming
Boosting
Stuart Andrews and Thomas Hofmann
Advances in Neural Information Processing Systems (NIPS 16), 2004. |
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Loss Functions and Optimization Methods for
Discriminative Learning of Label Sequences
Yasemin Altun, Thomas Hofmann, Mark Johnson
Empirical Methods in Natural Language Processing (EMNLP), 2003
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Hidden Markov Support Vector Machines
Yasemin Altun, Ioannis Tsochantaridis, Thomas Hofmann
20th International Conference on Machine Learning (ICML), 2003
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Large Margin Methods for Label Sequence Learning
Yasemin Altun, Thomas Hofmann
8th European Conference on Speech
Communication and Technology (EuroSpeech), 2003 |
pdf
slides
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Gaussian Latent Semantic Models for Collaborative Filtering
Thomas Hofmann
26th Annual International ACM SIGIR Conference, 2003
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Hierarchical Semantic Classification: Word Sense Disambiguation with World Knowledge
Massimiliano Ciaramita, Thomas Hofmann, Mark Johnson
18th International Joint Conference on Artificial Intelligence (IJCAI), 2003
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Text Categorization by Boosting Automatically Extracted Concepts
Lijuan Cai, Thomas Hofmann
26th Annual International ACM SIGIR Conference, 2003
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Learning Mappings to Discrete Output Spaces via Joint Feature Maps
Thomas Hofmann, Ioannis Tsochantaridis, Yasemin Altun
Proceedings Snowbird Workshop on Machine Learning, 2003
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| 2002 |
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Predicting CNS Permeability of Drug Molecules: Comparison of Neural Network and Support Vector Machine Algorithms
Scott Doninger, Thomas Hofmann, Joanne Yeh
Journal of Computational Biology, Volume 9, Number 6, 2002
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e-journal
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Discriminative Learning for Label Sequences via Boosting
Yasemin Altun, Thomas Hofmann & Mark Johnson
Advances in Neural Information Processing Systems (NIPS*15), 2003
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Support Vector Machines for Multiple-Instance Learning
Stuart Andrews, Ioannis Tsochantaridis & Thomas Hofmann
Advances in Neural Information Processing Systems (NIPS*15), 2003 |
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Support Vector Machines for Polycategorical Classification
Ioannis Tsochantaridis & Thomas Hofmann
European Conference on Machine Learning (ECML), 2002
(Best Paper Award)
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Statistical Pattern Recognition
Thomas Hofmann
Encyclopedia of Cognitive Sciences, Nature Publishing, 2002
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Multiple Instance Learning With Generalized Support Vector Machines
Stuart Andrews, Thomas Hofmann & Ioannis Tsochantaridis
Proceedings of AAAI Conference, Student Paper, 2002 - and -
Workshop Notes, Snowbird Learning Workshop, 2002
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| 2001 |
| J |
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Unsupervised Learning by Probabilistic Latent Semantic Analysis
Thomas Hofmann
Machine Learning Journal, 42(1), 2001, pp.177.196
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What People (Don't) Want
Thomas Hofmann
European Conference on Machine Learning (ECML), 2001.
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The Missing Link: A Probabilistic Model of Document Content and Hypertext Connectivity
David Cohn and Thomas Hofmann
Advances in Neural Information Processing Systems (NIPS*13), MIT Press 2001
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Text Classification in a Hierarchical Mixture Model for Small
Training Sets
Kristina Toutanova, Francine Chen, Kris Popat & Thomas Hofmann
Proceedings 10th International Conference on Information and
Knowledge Management, 2001
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Polycatgorical Classification
Thomas Hofmann & Ioannis Tsochantaridis
In: Proceedings of the Learning Workshop, Snowbird UT, 2001
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Dynamics,
Information, and the Web Environment
Thomas Dean, Jasminca Hasic, Thomas Hofmann, Gopal Pandurangan, Prabhakar Raghavan & Eli Upfal
In: DARPA TASK Proceedings, Santa Fe, NM, April 2001.
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| 2000 |
| J |
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ProbMap - A Probabilistic Approach for Mapping Large Document Collections
Thomas Hofmann
Journal for Intelligent Data Analysis, 4 (2000) pp.149-164 |
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A Theory of Proximity Based Clustering:
Structure Detection by Optimization
Jan Puzicha, Thomas Hofmann & Joachim Buhmann
Pattern Recognition, 33 (4) (2000) pp. 617-634 |
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Learning Probabilistic Models of the Web
Thomas Hofmann
Proceedings of the 23rd International Conference on Research and Development in Information Retrieval (ACM SIGIR'00)
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Learning Curved Multinomial Subfamilies for Natural Language Processing and Information Retrieval
Keith Hall & Thomas Hofmann
Proceeding of the International Conference for Machine Learning (ICML 2000)
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Learning the Similarity of Documents
Thomas Hofmann
Advances in Neural Information Processing Systems (NIPS*12), MIT Press 2000 |
pdf
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| 1999 |
| C |
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Probabilistic Latent Semantic Analysis
Thomas Hofmann
Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI'99)
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Probabilistic Latent Semantic Indexing
Thomas Hofmann
Proceedings of the 22nd International Conference on Research and Development in Information Retrieval (SIGIR'99) |
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Topic Based Language Models Using EM
Dan Gildea & Thomas Hofmann
Proceedings of 6th European Conference On Speech Communication and Technology (Eurospeech'99)
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Latent Class Models for Collaborative Filtering
Thomas Hofmann & Jan Puzicha
Proceedings of the International Joint Conference in Artificial Intelligence, 1999
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Histogram Clustering for Unsupervised Segmentation and Image Retrieval
Jan Puzicha, Thomas Hofmann & Joachim Buhmann
Pattern Recognition Letters, 1999
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Histogram Clustering for Unsupervised Image Segmentation
Jan Puzicha, Thomas Hofmann & Joachim Buhmann
Proceedings CVPR, 1999.
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Topological Maps for the Visualization of Large Document Collections
Thomas Hofmann
In: Advances in Intelligent Data Analysis (IDA), Lecture Notes in Computer Science 1642, Springer Verlag, pp. 161-172, 1999
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The Cluster-Abstraction Model: Unsupervised Learning of Topic Hierarchies from Text Data
Thomas Hofmann
Proceedings of the International Joint Conference in Artificial Intelligence, 1999
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| 1998 |
| J |
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Unsupervised Texture Segmentation in a Deterministic Annealing Framework
Thomas Hofmann, Jan Puzicha & Joachim Buhmann
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998
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Competitive Learning Algorithms for Robust Vector Quantization
Thomas Hofmann & Joachim Buhmann
IEEE Transaction on Signal Processing, Vol. 46, No. 6, June 1998
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Statistical Models for Co-occurrence Data
Thomas Hofmann & Jan Puzicha
AI Memo 1625, CBCL Memo 159, Artificial Intelligence Laboratory and
Center for Biological and Computational Learning,
MIT, February 1998
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Mixture Models for Co-occurrence and Histogram Data
Thomas Hofmann & Jan Puzicha
Proceedings of the ICPR'98, Brisbane, Australia
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Active Data Clustering
Thomas Hofmann & Joachim Buhmann
Advances in Neural Information Processing Systems (NIPS*10), MIT Press 1998
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Discrete Mixture Models for Unsupervised Image Segmentation
Jan Puzicha, Thomas Hofmann & Joachim Buhmann
Proceedings, 20th DAGM Symposion, Stuttgart, Germany, 1998
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Unsupervised Learning from Dyadic Data
Thomas Hofmann & Jan Puzicha
Technical Report, ICSI TR-98-042 |
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Learning and Representing Topic. A Hierarchical Mixture Model for Word Occurrences in
Document Databases.
Thomas Hofmann
In: Proceedings of the Conference for Automated Learning and Discovery (CONALD),
Pittsburgh, 1998.
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| 1997 |
| J |
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Pairwise Data Clustering by Deterministic Annealing
Thomas Hofmann & Joachim Buhmann
IEEE Transactions on Pattern Analysis and Machine Intelligence,
1997, Vol. 19(1), pp. 1-14 (and 19(2), p.192)
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Non-parametric Similarity Measures for Unsupervised
Texture Segmentation and Image Retrieval.
Jan Puzicha, Thomas Hofmann & Joachim M. Buhmann
In: Proceedings of the Conference on Computer Vision and Pattern
Recognition (CVPR'97), Santa Barbara, 1997
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Deterministic Annealing for Unsupervised Texture Segmentation
Thomas Hofmann, Jan Puzicha & Joachim Buhmann
Proceedings EMMCVPR'97, Venice, 1997
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Robust Vector Quantization by Competitive Learning
Thomas Hofmann & Joachim Buhmann
Proceedings International Conference on Acoustics, Speech, and
Signal Processing ICASSP97, Munich, 1997
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An Optimization Approach to Unsupervised Hierarchical Texture Segmentation
Thomas Hofmann & Joachim Buhmann
Proceedings of the International Conference on Image Processing (ICIP '97), Santa Barbara, 1997.
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Deterministic Annealing: Fast Physical Heuristics for
Real-Time Optimization of Large Systems.
Jan Puzicha, Thomas Hofmann & Joachim Buhmann
In: Proceedings of the 15th IMACS World Conference on Scientific
Computation, Modeling and Applied Mathematics, Berlin, 1997.
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| 1996 |
| B |
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Map Learning and High-Speed Navigation in RHINO
Sebastian Thrun, Arno Buecken, Wolfram Burgard, Dieter Fox, Thorsten Froehlinghaus, Daniel Hennig, Thomas Hofmann, Michael Krell & Timo Schmidt
in: AI-based Mobile Robots: Case studies of successful robot systems, Kortenkamp, D. and Bonasso, R.P. and Murphy, R. (eds.), MIT Press
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Inferring Hierarchical Clustering Structures by Deterministic Annealing
Thomas Hofmann & Joachim Buhmann
Proceedings Second International Conference on Knowledge Discovery and Data Mining KDD96 , Portland, Oregon, 1996
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Annealed Neural Gas Network for Robust Vector Quantization.
Thomas Hofmann & Joachim M. Buhmann
In: Proceedings of the International Conference on Artificial Neural Networks (ICANN), pp. 151-156, 1996.
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Unsupervised Segmentation of Textured Images by
Pairwise Data Clustering.
Thomas Hofmann, Jan Puzicha & Joachim M. Buhmann
In: Proceedings of the International Conference on Image Processing (ICIP), Lausanne,
Vol. III, pp. 137-140, 1996.
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| 1995 |
| J |
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The Mobile Robot RHINO
J. Buhmann, W. Burgard, A.B. Cremers, D. Fox, T. Hofmann, F. Schneider, J. Strikos & S. Thrun
AI Magazin, 16:1, 1995
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Hierarchical Pairwise Data Clustering by Mean-Field Annealing
Thomas Hofmann & Joachim Buhmann
Proceedings of the International Conference on Artificial Neural Networks (ICANN'95), pp. 197-202, 1995
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Multidimensional Scaling and Data Clustering.
Thomas Hofmann & Joachim M. Buhmann
In: Advances in Neural Information Processing Systems (NIPS 7), Morgan Kaufmann Publishers, 1995.
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| 1994 |
| C |
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A Maximum Entropy Approach to Pairwise Data Clustering.
Joachim M. Buhmann & Thomas Hofmann
In: Proceedings of the International Conference on Pattern Recognition (ICPR), Hebrew University, Jerusalem, vol.II,
IEEE Computer Society Press, pp. 207-212, 1994.
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Central and Pairwise Data Clustering by Competitive Neural
Networks.
Joachim M. Buhmann & Thomas Hofmann
In: Advances in Neural Information Processing Systems (NIPS 6), Morgan Kaufmann Publishers, pp. 104-111,
1994.
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