Large margin taxonomy embedding with an application to document categorization

Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference

Volumn , Issue , 2009, Pages 1737-1744

  Weinberger, Kilian ^a   Chapelle, Olivier ^a  

^a YAHOO RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; INFORMATION RETRIEVAL SYSTEMS;

CLASS HIERARCHIES; COST-SENSITIVE; DOCUMENT CATEGORIZATION; EMBEDDINGS; LARGE MARGINS; MULTI-CLASS CLASSIFICATION; NOVEL ALGORITHM; OPTIMISATIONS; SEMANTIC SPACE; STATE OF THE ART;

SEMANTICS;

EID: 84858773897     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (16)

1. D. Blei, A. Ng, M. Jordan, and J. Lafferty. Latent Dirichlet Allocation. Journal of Machine Learning Research, 3(4-5):993-1022, 2003.
2. S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press, 2004.
3. A. Budanitsky and G. Hirst. Semantic distance in wordnet: An experimental, application-oriented evaluation of five measures. In Workshop on WordNet and Other Lexical Resources, in the North American Chapter of the Association for Co mputational Linguistics (NAACL), 2001.
4. L. Cai and T. Hofmann. Hierarchical document categorization with support vector machines. In ACM 13th Conference on Information and Knowledge Management, 2004.
5. T. Cox and M. Cox. Multidimensional Scaling. Chapman & Hall, London, 1994.
6. K. Crammer and Y. Singer. On the algorithmic implementation of multiclass kernel-based vector machines. Journal of Machine Learning Research, 2:265-292, 2001.
7. S. Deerwester, S. Dumais, G. Furnas, T. Landauer, and R. Harshman. Indexing by latent semantic analysis. Journal of the American Society for Information Science, 41(6):391-407, 1990.
8. S. Dumais and H. Chen. Hierarchical classification of Web content. In Proceedings of SIGIR-00, 23rd ACM International Conference on Research and Development in Information Retrieval, pages 256-263. ACM Press, New York, US, 2000.
9. W. Hersh, C. Buckley, T. J. Leone, and D. Hickam. OHSUMED: an interactive retrieval evaluation and new large test collection for research. In SIGIR '94: Proceedings of the 17th annual international ACM conference on Research and development in information retrieval, pages 192-201. Springer-Verlag New York, Inc., 1994.
10. G. Karypis, E. Hong, and S. Han. Concept indexing a fast dimensionality reduction algorithm with applications to document retrieval & categorization, 2000. Technical Report: 00-016 karypis, han@cs.umn.edu Last updated on.
11. T.-Y. Liu, Y. Yang, H. Wan, H.-J. Zeng, Z. Chen, and W.-Y. Ma. Support vector machines classification with a very large-scale taxonomy. SIGKDD Explorations Newsletter, 7(1):36-43, 2005.
12. R. Rifkin and A. Klautau. In Defense of One-Vs-All Classification. The Journal of Machine Learning Research, 5:101-141, 2004.
13. F. Sebastiani. Machine learning in automated text categorization. ACM Computing Surveys, 34(1):1-47, 2002.
14. F. Sha and L. K. Saul. Large margin hidden markov models for automatic speech recognition. In Advances in Neural Information Processing Systems 19, Cambridge, MA, 2007. MIT Press.
15. A. Weigend, E. Wiener, and J. Pedersen. Exploiting Hierarchy in Text Categorization. Information Retrieval, 1(3):193-216, 1999.
16. K. Q. Weinberger and L. K. Saul. Fast solvers and efficient implementations for distance metric learning. pages 1160-1167, 2008.