Learning to classify by ongoing feature selection

Image and Vision Computing

Volumn 28, Issue 4, 2010, Pages 715-723

  Levi, Dan ^a   Ullman, Shimon ^a  

^a WEIZMANN INSTITUTE OF SCIENCE   (Israel)

Author keywords

Object recognition; Online learning

Indexed keywords

FEATURE EXTRACTION; OBJECT RECOGNITION;

ADAPTIVE CLASSIFICATION; CLASSIFICATION ALGORITHM; CLASSIFICATION FEATURES; CLASSIFICATION PERFORMANCE; FEATURE SELECTION; FUTURE APPLICATIONS; LINE FEATURES; MUTUAL INFORMATIONS; ON-LINE PROCESS; ONGOING LEARNING; ONLINE LEARNING; TRAINING DATA; TRAINING EXAMPLE; TRAINING PROCESS; TRAINING SETS;

CLASSIFICATION (OF INFORMATION);

EID: 74749091012     PISSN: 02628856     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.imavis.2008.10.010     Document Type: Article

References (15)

1. Guyon I., and Elisseeff A. An introduction to variable and feature selection. J. Mach. Learn. Res. 3 (2003) 1157-1182
2. Fleuret F. Fast binary feature selection with conditional mutual information. J. Mach. Learn. Res. 5 (2004) 1531-1555
3. N. Vasconcelos, M. Vasconcelos, Scalable discriminant feature selection for image retrieval and recognition, in: CVPR, No. 2, 2004, pp. 770-775.
4. Blum A., and Langley P. Selection of relevant features and examples in machine learning. Artif. Intell. 97 1-2 (1997) 245-271. citeseer.ist.psu.edu/blum97selection.html
5. Kohavi R., and John G.H. Wrappers for feature subset selection. Artif. Intell. 97 1-2 (1997) 273-324. citeseer.ist.psu.edu/kohavi96wrappers.html
6. R. Battiti, Using mutual information for selecting features in supervised neural net learning, IEEE Trans. Neural Networks 5 (4).
7. N. Kwak, C.H. Choi, Improved mutual information feature selector for neural networks in supervised learning, in: Proc. of the 1999 Int. Joint Conf. on Neural Networks, 1999.
8. M. Vidal-Naquet, S. Ullman, Object recognition with informative features and linear classification., in: ICCV, 2003, pp. 281-288.
9. Fano R. Transmission of Information: A Statistical theory of Communications (1961), Wiley, New York, NY
10. Principe J., Xu D., and Fisher J. Information Theoretic Learning. In: Haykin S. (Ed). Unsupervised Adaptive Filtering (1999), Wiley, New York, NY
11. S. Perkins, J. Theiler, Online feature selection grafting, in: Proc. of the 20th Int. Conf. on Machine Learning, 2003, pp. 21-24.
12. Jain A., and Zongker D. Feature selection: evaluation, application, and small sample performance. IEEE Trans. Pattern Anal. Mach. Intell. 19 2 (1997) 153-158
13. R. Fergus, P. Perona, A. Zisserman, Object class recognition by unsupervised scale-invariant learning, in: Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, vol. 2, Madison, Wisconsin, 2003, pp. 264-271.
14. Lowe D.G. Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60 2 (2004) 91-110
15. Pearl J. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference (1988), Morgan Kaufmann, Los Altos, CA