Similarity learning for nearest neighbor classification

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2008, Pages 983-988

  Qamar, Ali Mustafa ^a   Gaussier, Eric ^a   Chevallet, Jean Pierre ^b   Lim, Joo Hwee ^b  

^a UNIV GRENOBLE ALPES   (France)

^b INSTITUTE FOR INFOCOMM RESEARCH   (Singapore)

Author keywords

[No Author keywords available]

Indexed keywords

COSINE MEASURE; GENERAL CLASS; JACCARD COEFFICIENTS; KNN CLASSIFICATION; LEARNING SIMILARITY; NEAREST NEIGHBOR CLASSIFICATION; PERCEPTRON ALGORITHMS; PREDICTION RULES; SIMILARITY FUNCTIONS; SIMILARITY MEASURE;

DATA MINING; EDUCATION; INFORMATION MANAGEMENT; MINING;

LEARNING ALGORITHMS;

EID: 67049162745     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2008.81     Document Type: Conference Paper

References (16)

1. J.-P. Bao, J.-Y. Shen, X.-D. Liu, and H.-Y. Liu. Quick asymmetric text similarity measures. Machine Learning and Cybernetics, 2003 International Conference on, 2003.
2. L. Baoli, L. Qin, and Y. Shiwen. An adaptive k-nearest neighbor text categorization strategy. ACM Transactions on Asian Language Information Processing (TALIP), 2004.
3. M. Collins. Discriminative training methods for hidden markov models: theory and experiments with perceptron algorithms. In EMNLP '02: Proceedings of the Conference on Empirical methods in natural language processing, 2002.
4. J. V. Davis, B. Kulis, P. Jain, S. Sra, and I. S. Dhillon. Information-theoretic metric learning. In Proceedings of the 24th International Conference on Machine Learning, 2007.
5. O. Dekel and Y. Singer. Data-driven online to batch conversions. In NIPS, 2005.
6. C. B. D.J. Newman, S. Hettich and C. Merz. UCI repository of machine learning databases, 1998.
7. Y. Freund and R. E. Schapire. Large margin classification using the perceptron algorithm. Mach. Learn., 37(3), 1999.
8. A. Globerson and S. Roweis. Metric learning by collapsing classes. In Proceedings of Advances in Neural Information Processing Systems (NIPS), 2005.
9. M. Grabowski and A. Szalas. A technique for learning similarities on complex structures with applications to extracting ontologies. In J. Szczepaniak, P.S. Kacprzyk and A. Niewiadomski, editors, Proceedings of the 3rd Atlantic Web Intelligence Conf. AWIC 2005, LNAI. Springer Verlag, 2000.
10. D. P. Helmbold and M. K. Warmuth. On weak learning. J. Comput. Syst. Sci., 50(3), 1995.
11. A. Hust. Learning Similarities for Collaborative Information Retrieval. In Proceedings of KI 2004 workshop "Machine Learning and Interaction for Text-Based Information Retrieval", TIR-04, pages 43-54, September 2004.
12. L. R. M. Diligenti, M. Maggini. Learning similarities for text documents using neural networks. In Proceedings of IAPR - TC3 International Workshop on Artificial Neural Networks in Pattern Recognition (ANNPR 2003, 2003.
13. A. K. McCallum. Bow: A toolkit for statistical language modeling, text retrieval, classification and clustering, 1996.
14. R. Nock,M. Sebban, and D. Bernard. A simple locally adaptive nearest neighbor rule with application to pollution forecasting. International Journal for Pattern Reconition and Artificial Intelligence, 17(8), 2003.
15. S. Shalev-Shwartz, Y. Singer, and A. Y. Ng. Online and batch learning of pseudo-metrics. In ICML '04: Proceedings of the twenty-first international conference on Machine learning, New York, NY, USA, 2004. ACM.
16. K.Weinberger, J. Blitzer, and L. Saul. Distance metric learning for large margin nearest neighbor classification. In Advances in Neural Information Processing Systems 18. 2006.