Subspace classifier in the Hilbert space

Pattern Recognition Letters

Volumn 20, Issue 5, 1999, Pages 513-519

  Tsuda, Koji ^a  

^a ELECTROTECHNICAL LABORATORY   (Japan)

Author keywords

Hilbert space; Kernel functions; Pattern recognition; Subspace classifier; Support vector machine

Indexed keywords

ERROR ANALYSIS; FUNCTIONS; OPTIMIZATION; PATTERN MATCHING; PROBLEM SOLVING;

GAUSSIAN FUNCTIONS; HILBERT SPACES; KERNEL FUNCTIONS; SUBSPACE CLASSIFIERS; SUPPORT VECTOR MACHINES (SVM);

OPTICAL CHARACTER RECOGNITION;

EID: 0032669003     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0167-8655(99)00023-9     Document Type: Article

References (11)

1. Bellman, R., 1970. Introduction to Matrix Analysis, 2nd Ed. McGraw-Hill, New York.
2. Deshuang H. The statistical properties of the learning subspace methods for pattern recognition. Trans. Inform. Process. Soc. Jpn. 37(6):1996;1081-1087.
3. Haykin, S., 1995. Neural Networks: A Comprehensive Foundation. IEEE Computer Society Press, Silver Spring, MD.
4. Kimura F., Takashina K., Tsuruoka S., Miyake Y. Modified quadratic discriminant functions and the application to chinese character recognition. IEEE Trans. Patt. Anal. Mach. Intell. 9(1):1987;149-153.
5. Oja, E., 1983. Subspace Methods of Pattern Recognition. Research Studies Press.
6. Osuna, E.E., Freund, R., Girosi, F., 1997. Support vector machines: Training and applications. MIT AI Memo 1602.
7. Prakash M., Murty M.N. Extended subspace methods of pattern recognition. Pattern Recognition Letters. 17(11):1996;1131-1139.
8. Prakesh M., Murty M.N. Hebbian learning subspace method: A new approach. Pattern Recognition. 30(1):1997;141-149.
9. Small, C.G., 1994. Hilbert Space Methods in Probability and Statistical Inference. Wiley, New York.
10. Scholkopf B., Sung K.K., Burges C.J.C., Girosi F., Niyogi P., Poggio T., Vapnik V. Comparing support vector machines with gaussian kernels to radial basis function classifiers. IEEE Trans. Signal Process. 45(11):1997;2758-2765.
11. Vapnik, V.N., 1995. The Nature of Statistical Learning Theory. Springer, Berlin.