Towards expanding relevance vector machines to large scale datasets

International Journal of Neural Systems

Volumn 18, Issue 1, 2008, Pages 45-58

  Silva, Catarina ^a,b   Ribeiro, Bernardete ^b  

^a UNIVERSITY OF COIMBRA   (Portugal)

^b POLYTECHNIC INSTITUTE OF LEIRIA   (Portugal)

Author keywords

Large scale learning; Relevance vector machines; Text classification

Indexed keywords

BAYESIAN NETWORKS; CLASSIFICATION (OF INFORMATION); DATABASE SYSTEMS; INFERENCE ENGINES; LARGE SCALE SYSTEMS; PROBABILITY;

LARGE SCALE LEARNING; RELEVANCE VECTOR MACHINES; TEXT CLASSIFICATION;

SUPPORT VECTOR MACHINES;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BAYES THEOREM; HUMAN; INFORMATION RETRIEVAL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; HUMANS; INFORMATION STORAGE AND RETRIEVAL; PATTERN RECOGNITION, AUTOMATED;

EID: 43949132994     PISSN: 01290657     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0129065708001361     Document Type: Conference Paper

References (31)

1. F. Sebastiani, Classification of text, automatic, The Encyclopedia of Language and Linguistics, in Keith Brown (ed.), Vol. 14, 2nd edn., Elsevier (2006).
2. V. Vapnik, The Nature of Statistical Learning Theory, 2nd edn., Springer (1999).
3. M. Tipping, Sparse Bayesian learning and the relevance vector machine, Journal of Machine Learning Research I (2001) 211-214.
4. S. Eyheramendy, A. Genkin, W. Ju, D. Lewis and D. Madigan, Sparse Bayesian classifiers for text classification, Journal of Lntelligence Community Research and Development (2003).
5. F. Sebastiani, Machine learning in automated text categorization, ACM Computing Surveys 34 (2002) 1-47.
6. L. Kuncheva, Combining Pattern Classifiers: Methods and Algorithms (Wiley, 2004).
7. Y. Yang, J. Zhang and B. Kisiel, A scalability analysis of classifiers in text categorization, SIGIR '03, ACM Press, (2003), 96-103.
8. T. Joachims, Learning to Classify Text. Using SVM (Kluwer, 2002).
9. M. Seeger, C. Williams and N. Lawrence, Fast forward selection to speed up sparse Gaussian process regression, International Workshop on AI and Statistics (2003).
10. L. Csató and M. Opper, Sparse online Gaussian processes, Neural Computation 14 (2002) 641-668.
11. A. Smola and P. Bartlett, Sparse Greedy Gaussian processes regression, Advances in Neural Information Processing 13 (2001) 619-625.
12. J. Candela, Learning with uncertainty - gaussian processes and relevance vector machines, PhD thesis, Technical University of Denmark (2004).
13. M. Tipping and A. Faul, Fast marginal likelihood maximisation for sparse Bayesian models, International Workshop on Artificial Intelligence and Statistics (2003).
14. D. H. Wolpert and W. G. Macready, No free lunch theorems for optimization, IEEE Transactions on Evolutionary Computation 1 (1997) 67.
15. R. Cooley, Classification of news stories using support vector machines, IJCAI'99 Workshop on Text Mining, Stockholm, Sweden, August (1999).
16. C. Silva and B. Ribeiro, On text-based mining with active learning and background knowledge using SVM, Journal of Soft Computing - A Fusion of Foundations, Methodologies and Applications, Springer Verlag, April (2006).
17. T. Joachims, Text categorization with support vector machines: Learning with many relevant features, European Conference on Machine Learning (1998) 137-142.
18. O. Williams, A. Blake and R. Cipolla, Sparse Bayesian learning for efficient visual tracking, IEEE Transactions on Pattern Analysis and Machine Intelligence 27(8) (2005) 1292-1304.
19. L. Wei, Y. Yang, R. Nishikawa, M. Wernick and A. Edwards, Relevance vector machine for automatic detection of clustered microcalcifications, IEEE Transactions on Medical Imaging 24(10) (2005) 1278-1285.
20. C Silva and B. Ribeiro, Scaling text classification with relevance vector machines. IEEE Intl. Conference on Systems Man and Cybernetics (2006) 4186-4191.
21. B. Schölkopf, C. Burges and A. Smola, Advances in Kernel Methods: Support Vector Machines (Cambridge, MIT Press, 1998).
22. R. Johansson and P. Nugues, Sparse Bayesian classification of predicate arguments, CoNLL-2005: Proceedings of the Ninth Conference on Computational Natural Language Learning, 43rd Annual Meeting of the Association of Computational Linguistics (2005).
23. R,. Schapire and Y. Singer, Boostexter: A boosting based system for text categorization, Machine Learning 39(2/3) (2000) 135-168.
24. Y. Freund and R. Schapire, Experiments with a new boosting algorithm, International Conference Machine Learning (1996) 148-156.
25. T. Windeatt, Accuracy/diversity and ensemble MLP classifier design, IEEE Transactions on Neural Networks 17(5) (2006) 1194-1210.
26. C. Apté, F. Damerau and S. Weiss, Automated learning of decision rules for text categorization, ACM Transactions for Information Systems 12 (1994) 233-251.
27. D. Lewis, An evaluation of phrasal and clustered representations on a text categorization task, 15th International ACMSIGIR Conference on Research and Development in Information Retrieval (1992) 37-50.
28. C. van Rijsbergen, Information Retrieval, 2nd edn. (Butterworths, London, 1979).
29. M. Ruiz and P. Srinivasan, Hierarchical text categorization using neural networks, Information Retrieval 5 (2002) 87-118.
30. T. Fawcett, ROC graphs: Notes and practical considerations for data mining researchers, HP Laboratories (2004).
31. M. F. Porter, An algorithm for suffix stripping, Program 14(3) (1980) 130-137.