On the relationship between the Support Vector Machine for classification and sparsified Fisher's Linear Discriminant

Neural Processing Letters

Volumn 9, Issue 2, 1999, Pages 129-139

  Shashua, Amnon ^a  

^a HEBREW UNIVERSITY OF JERUSALEM   (Israel)

Author keywords

[No Author keywords available]

Indexed keywords

LEARNING SYSTEMS; SET THEORY; VECTORS;

FISHER'S LINEAR DISCRIMINANT; SUPPORT VECTOR MACHINE (SVM);

PATTERN RECOGNITION;

EID: 0032648658     PISSN: 13704621     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1018677409366     Document Type: Article

References (16)

1. P.N Belhumeur, J.P. Hespanha, and D.J. Kriegman, Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection, in Proc. European Conference on Computer Vision, 1996.
2. B.E. Boser, I.M. Guyon, and V.N. Vapnik, A training algorithm for optimal margin classifier, in Proc. 5th Workshop on Computational Learning Theory, pp. 144-152, 1992.
3. S. Chen and D. Donoho, Atomic decomposition by basis pursuit, Technical Report Dept. of Statistics, TR-479, Stanford, 1995.
4. C. Cortes and V.N. Vapnik, Support vector networks, Machine Learning 20, 1-25, 1995.
5. R. Courant and D. Hilbert, Methods of Mathematical Physics, Interscience Publishers Inc., 1953.
6. R.O. Duda and P.E. Hart, Pattern Classification and Scene Analysis, John Wiley, New York, 1973.
7. F. Girosi, An equivalence between sparse approximation and support vector machines, Technical Report AI Memo 1606, MIT, 1997.
8. G.F. Harpur and R.W. Prager, Development of low entropy coding in a recurrent network, Network, 7, 277-284, 1996.
9. D.W. Jacobs and D. Weinshall, Classifying images using non-metric distances, in Proc. International Conference on Computer Vision, January 1998.
10. D.G. Luenberger, Linear and Nonlinear Programming, Addison-Wesley, 1937.
11. P. Meer, D. Mintz, D. Kim, and A. Rosenfeld, Robust regression methods for computer vision: A review, International Journal of Computer Vision 6(1), 59-70, 1991.
12. B.A. Olshausen and D.J. Field, Emergence of simple-cell receptive field properties by learning a sparse code for natural images, Nature 381(13), 1996.
13. E. Osuna, R. Freund, and F. Girosi, Training support vector machines: An application to face detection, in Proc. IEEE Conference on Computer Vision and Pattern Recognition, 1997.
14. M. Pontil, S. Rogai, and A. Verri, Recognizing 3D objects with linear Support Vector Machines, in Proc. 5th European Conference on Computer Vision (ECCV), pp. 469-479, June, 1998.
15. A. Tversky, Features of similarity, Psychological Review 84(4), 327-352, 1977.
16. V.N. Vapnik, The Nature of Statistical Learning, Springer, 1995.