Learning to select features using their properties

Journal of Machine Learning Research

Volumn 9, Issue , 2008, Pages 2349-2376

  Krupka, Eyal ^a   Navot, Amir ^a   Tishby, Naftali ^a  

^a HEBREW UNIVERSITY OF JERUSALEM   (Israel)

Author keywords

Feature selection; Meta features; Unobserved features

Indexed keywords

FORECASTING;

FEATURE MEASUREMENTS; FEATURE SELECTION; FEATURE SELECTIONS; GENERALIZATION BOUNDS; HANDWRITTEN DIGIT RECOGNITIONS; LEARNING PROBLEMS; META-FEATURES; OBJECT CATEGORIES; SELECTION ALGORITHMS; TRAINING INSTANCES; UNOBSERVED FEATURES; VISUAL OBJECTS;

OBJECT RECOGNITION;

EID: 56349093380     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article

References (34)

1. P.L. Bartlett. The size of the weights is more important than the size of the network. IEEE Transactions on Information Theory, 1998.
2. A. Blum and J. Langford. Pac-mdl bounds. Learning Theory and Kernel Machines, 2003.
3. K. Crammer. Mcsvm_1.0: C code for multiclass svm, 2003. http://www.cis.upenn.edu/~crammer.
4. D. Decoste and B. Schölkopf. Training invariant support vector machines. Machine Learning, 2002.
5. L. Ein-Dor, O. Zuk, and E. Domany. Thousands of samples are needed to generate a robust gene list for predicting outcome in cancer. Proceedings of the National Academy of Sciences, 2006.
6. D. Gabor. Theory of communication. J. IEE, 93:429-459, 1946.
7. R. Gilad-Bachrach, A. Navot, and N. Tishby. Margin based feature selection - theory and algorithms. In International Conference on Machine Learning (ICML), 2004.
8. R. Greiner. Using value of information to learn and classify under hard budgets. In NIPS Workshop on Value of Information in Inference, Learning and Decision-Making, 2005.
9. I. Guyon and A. Elisseeff. An introduction to variable and feature selection. Journal of Machine Learning Research, 2003.
10. I. Guyon, J. Weston, S. Barnhill, and V. Vapnik. Gene selection for cancer classification using support vector machines. Machine Learning, 46, 2002.
11. M. W. Kadous and C. Sammut. Classification of multivariate time series and structured data using constructive induction. Machine Learning, 2005.
12. M. J. Kearns and U. V. Vazirani. An Introduction to Computational Learning Theory. MIT Press, Cambridge, MA, USA, 1994.
13. R. Kohavi and G.H. John. Wrapper for feature subset selection. Artificial Intelligence, 97(1-2): 273-324, 1997.
14. E. Krupka and N. Tishby. Generalization from observed to unobserved features by clustering. Journal of Machine Learning Research, 2008.
15. E. Krupka and N. Tishby. Incorporating prior knowledge on features into learning. In International Conference on Artificial Intelligence and Statistics (AISTATS), 2007.
16. E. Kussul, T. Baidyk, L. Kasatkina, and V. Lukovich. Rosenblatt perceptrons for handwritten digit recognition. In Int'l Joint Conference on Neural Networks, pages 1516-20, 2001.
17. F. Lauer and G. Bloch. Incorporating prior knowledge in support vector machines for classification: a review. Submitted to Neurocomputing, 2006.
18. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324, November 1998.
19. S. Lee, V. Chatalbashev, D. Vickrey, and D. Koller. Learning a meta-level prior for feature relevance from multiple related tasks. In International Conference on Machine Learning (ICML), 2007.
20. K. Levi, M. Fink, and Y. Weiss. Learning from a small number of training examples by exploiting object categories. LCVPR04 workshop on Learning in Computer Vision, 2004.
21. D. Lizotte, O. Madani, and R. Greiner. Budgeted learning of naive-bayes classifiers. In Conference on Uncertainty in Artificial Intelligence (UAI), 2003.
22. A. Navot, L. Shpigelman, N. Tishby, and E. Vaadia. Nearest neighbor based feature selection for regression and its application to neural activity. In Advances in Neural Information Processing Systems (NIPS), 2006.
23. J. R. Quinlan. Induction of decision trees. In Jude W. Shavlik and Thomas G. Dietterich, editors, Readings in Machine Learning. Morgan Kaufmann, 1990.
24. R. Raina, A.Y. Ng, and D. Koller. Constructing informative priors using transfer learning. In Proc. Twenty-Third International Conference on Machine Learning, 2006.
25. T Serre, L. Wolf, and T. Poggio. Object recognition with features inspired by visual cortex. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2005.
26. T. Serre, L. Wolf, S. Bileschi, M. Riesenhuber, and T. Poggio. Robust object recognition with cortex-like mechanisms. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007.
27. S. Shalev-Shwartz and Y. Singer. Efficient learning of label ranking by soft projections onto polyhedra. Journal of Machine Learning Research, 2006.
28. P. Simard, Y LeCun, J. S. Denker, and B. Victorri. Transformation invariance in pattern recognition-tangent distance and tangent propagation. In Neural Networks: Tricks of the Trade, 1996.
29. P. Y. Simard, Y. A. Le Cun, and Denker. Efficient pattern recognition using a new transformation distance. In Advances in Neural Information Processing Systems (NIPS), 1993.
30. B. Taskar, M. F. Wong, and D. Koller. Learning on the test data: Leveraging unseen features. In International Conference on Machine Learning (ICML), 2003.
31. S. Ullman, M. Vidal-Naquet, and E. Sali. Visual features of intermediate complexity and their use in classification. Nature Neuroscience, 2002.
32. V. N. Vapnik. The Nature Of Statistical Learning Theory. Springer-Verlag, 1995.
33. V. N. Vapnik. Statistical Learning Theory. Wiley, 1998.
34. J. Weston, S. Mukherjee, O. Chapelle, M. Pontil, T. Poggio, and V. Vapnik. Feature selection for svms. In Advances in Neural Information Processing Systems (NIPS), 2000.