Improving naive bayes using class-conditional ICA

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 2527, Issue , 2002, Pages 1-10

  Bressan, Marco ^a   Vitrià, Jordi ^a  

^a UNIVERSITAT AUTÒNOMA DE BARCELONA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLASSIFIERS; LEARNING SYSTEMS; INFORMATION RETRIEVAL;

CONDITIONAL DISTRIBUTION; INDEPENDENCE ASSUMPTION; INDEPENDENT COMPONENT ANALYSIS(ICA); JOINT DENSITIES; KULLBACK-LEIBLER DISTANCE; MARGINAL DENSITIES; MUTUAL INFORMATIONS; PUBLIC DATABASE; MACHINE-LEARNING; NAIVE BAYES; RANDOM VECTORS;

INDEPENDENT COMPONENT ANALYSIS;

EID: 79952270109     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/3-540-36131-6_1     Document Type: Conference Paper

References (24)

1. Yang, Y., Slattery, S., Ghani, R.: A study of approaches to hypertext categorization. Journal of Intelligent Information Systems. Kluwer Academic Press (2002)
2. Lewis, D.: Naive bayes at forty: The independence assumption in information retrieval. In N'edellec, C., Rouveirol, C., eds.: Proceedings of ECML-98, 10th European Conference on Machine Learning. Volume 1398.25., Springer Verlag, Heidelberg, DE (1998) 4-15
3. Domingos, P., Pazzani, M.J.: On the optimality of the simple bayesian classifier under zero-one loss. Machine Learning 29 (1997) 103-130
4. Turtle, H., Croft, W.: Evaluation of an inference network-based retrieval model. ACM Transactions on Information Systems 9 (1991) 187-222
5. Rish, I., Hellerstein, J., Thathachar, J.: An analysis of data characteristics that affect naive bayes performance. In N'edellec, C., Rouveirol, C., eds.: Proceedings of the Eighteenth Conference on Machine Learning-ICML2001, Morgan Kaufmann (2001)
6. M.Bressan, D.Guillamet, J.Vitria: Using an ica representation of high dimensional data for object recognition and classification. In: IEEE CSC in Computer Vision and Pattern Recognition (CVPR 2001). Volume 1. (2001) 1004-1009
7. Bell, A., Sejnowski, T.: An information-maximization approach for blind signal separation. Neural Computation 7 (1995) 1129-1159
8. Field, D.: What is the goal of sensory coding? Neural Computation 6 (1994) 559-601
9. Hyvärinen, A.: Sparse code shrinkage: Denoising of nongaussian data by maximum likelihood estimation. Neural Computation 11 (1999) 1739-1768
10. Vigario, R., Jousmäki, V., Hämäläinen, M., Hari, R., Oja, E.: Independent component analysis for identification of artifacts in magnetoencephalographic recordings. Advances in Neural Information Processing Systems 10 (1998) 229-235
11. Blake, C., Merz, C.: Uci repository of machine learning databases (1998)
12. LeCun, Y., Labs-Research, A.: The MNIST DataBase of Handwritten digits. http://www.research.att.com/yann/ocr/mnist/index.html (1998)
13. Scott, D.W.: Multivariate Density Estimation. John Wiley and sons, New York, NY (1992)
14. Duda, R., Hart, P., Stork, D.: Pattern Classication. John Wiley and Sons, Inc., New York, 2nd edition (2001)
15. Simpson, E.: The interpretation of interaction in contingency tables. Journal of the Royal Statistical Society, Ser. B 13 (1951) 238-241
16. Bell, A., Sejnowski, T.: The 'independent components' of natural scenes are edge filters. Neural Computation 11 (1999) 1739-1768
17. Lee, T., Lewicki, M., Seynowski, T.: A mixture models for unsupervised classification of non-gaussian sources and automatic context switching in blind signal separation. IEEE Transactions on PAMI 22 (2000) 1-12
18. Hyvärinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. John Wiley and Sons (2001)
19. Comon, P.: Independent component analysis-a new concept? Signal Processing 36 (1994) 287-314
20. Hyvärinen, A.: New approximatins of differential entropy for independent component analysis and projection pursuit. Advances in Neural Processing Systems 10 (1998) 273-279
21. Marill, T., Green, D.: On the effectiveness of receptors in recognition systems. IEEE Trans. on Information Theory 9 (1963) 1-17
22. Kailath, T.: The divergence and bhattacharyya distance measures in signal selection. IEEE Trans. on Communication Technology COM-15 1 (1967) 52-60
23. Swain, P., King, R.: Two effective feature selection criteria for multispectral remote sensing. In: Proceedings of the 1st International Joint Conference on Pattern Recognition, IEEE 73 CHO821-9. (1973) 536-540
24. Swain, P., Davis, S.: Remote sensing: the quantitative approach. McGraw-Hill (1978)