Modeling category structures with a kernel function

Proceedings of the 8th Conference on Computational Natural Language Learning, CoNLL 2004 - Held in cooperation with HLT-NAACL 2004

Volumn , Issue , 2004, Pages

  Takamura, Hiroya ^a   Matsumoto, Yuji ^b   Yamada, Hiroyasu ^c  

^a TOKYO INSTITUTE OF TECHNOLOGY   (Japan)

^b NARA INSTITUTE OF SCIENCE AND TECHNOLOGY   (Japan)

^c JAPAN ADVANCED INSTITUTE OF SCIENCE AND TECHNOLOGY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

MIXTURES; NATURAL LANGUAGE PROCESSING SYSTEMS; SEMANTICS; SUPPORT VECTOR MACHINES;

COMPONENT MODELING; KERNEL FUNCTION; MIXTURE MODELING; NEGATIVE EXAMPLES; ONE-DIMENSIONAL; POSITIVE EXAMPLES; POSTERIOR LOG-ODDS; PROBABILISTIC MODELS; TANGENT VECTORS; TEXT CATEGORIZATION;

TEXT PROCESSING;

EID: 67649928666     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (9)

1. Susan T. Dumais, John Platt, David Heckerman, and Mehran Sahami. 1998. Inductive learning algorithms and representations for text categorization. In Proceedings of the Seventh International Conference on Information and Knowledge Management (ACMCIKM98), pages 148–155.
2. Thomas Hofmann. 1999. Probabilistic Latent Semantic Indexing. In Proceedings of the 22nd Annual ACM Conference on Research and Development in Information Retrieval, pages 50–57, Berkeley, California, August.
3. Thomas Hofmann. 2000. Learning the similarity of documents: An information geometric approach to document retrieval and categorization. In Advances in Neural Information Processing Systems, 12, pages 914–920.
4. Tommi Jaakkola and David Haussler. 1998. Exploiting generative models in discriminative classifiers. In Advances in Neural Information Processing Systems 11, pages 487–493.
5. Thorsten Joachims. 1998. Text categorization with support vector machines: Learning with many relevant features. In Proceedings of the 10th European Conference on Machine Learning, pages 137–142.
6. Robert E. Kass and Paul W. Vos. 1997. Geometrical foundations of asymptotic inference. New York: Wiley.
7. Koji Tsuda and Motoaki Kawanabe. 2002. The leave-one-out kernel. In Proceedings of International Conference on Artificial Neural Networks, pages 727–732.
8. Koji Tsuda, Motoaki Kawanabe, Gunnar Rätsch, Sören Sonnenburg, and Klaus-Robert Müller. 2002. A new discriminative kernel from probabilistic models. Neural Computation, 14(10):2397–2414.
9. Vladimir Vapnik. 1998. Statistical Learning Theory. John Wiley, New York.