Least squares Littlewood-Paley wavelet support vector machine

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3789 LNAI, Issue , 2005, Pages 462-472

  Wu, Fangfang ^a   Zhao, Yinliang ^a  

^a XI'AN JIAOTONG UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

DATABASE SYSTEMS; FUNCTIONS; LEARNING SYSTEMS; REGRESSION ANALYSIS; WAVELET TRANSFORMS;

GENERALIZATION ABILITY; ORTHONORMAL FUNCTIONS; SUPPORT VECTOR MACHINES (SVM); WAVELET DECOMPOSITION;

LEAST SQUARES APPROXIMATIONS;

EID: 33646801221     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11579427_47     Document Type: Conference Paper

References (12)

1. Vapnik, V. The Nature of Statistical Learning Theory. 1995. New York: Springer-Verlag 1-175.
2. Zhang, X.G. Introduction to Statistical Learning Therory and Support Vector Machines. Acta Automatica Snica. 2000, 26(1): 32-42.
3. Burges, C.J.C. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery. 1998, 2(2): 955-974.
4. Bernhard S., Sung K.K. Comparing Support Vector Machines with Gaussian Kernels to Radical Basis Fuction Classifiers. IEEE Transaction on Signal Processing. 1997, 45(11): 2758-2765.
5. Edgar O., Robert F., Federico G. Training Support Vector Machines: An Application to Face Detection. IEEE Conference on Computer Vision and Pattern Recognition. 1997. 130-136.
6. Mercer J. Function of positive and negative type and their connection with the theory of integral equations [J]. Philosophical Transactions of the Royal Society of London: A, 1909, (209):415-446.
7. Stein, E.M Topics in Harmonic Analysis Related to the Littlewood-Paley Theory, Princeton University Press and the University of Tokio Press, Princeton, New Jersey, 1970.
8. Suykens J A K, Vandewalle J. Least squares support vector machine classifiers [J]. Neural Processing Letter, 1999, 9(3): 293-300.
9. Burges C J C. Geometry and invariance in kernel based methods [A]. in Advance in Kernel Methods-Support Vector Learning[C]. Cambridge, MA: MIT Press, 1999. 89-116.
10. Smola A., Schölkopf B., Müller K R. The connection between regularization operators and support vector kernels [J]. Neural Networks, 1998, 11(4):637-649.
11. Zhang Q, Benveniste A. Wavelet networks [J]. IEEE Trans on Neural Networks, 1992, 3(6): 889-898.
12. ftp://ftp.ics.uci.edu.cn/pub/machine-learning-database/adult.