Model Selection for K-Nearest Neighbors Regression Using VC Bounds

Proceedings of the International Joint Conference on Neural Networks

Volumn 2, Issue , 2003, Pages 1143-1148

  Cherkassky, Vladimir ^a   Ma, Yunqian ^a   Tang, Jun ^a  

^a UNIVERSITY OF MINNESOTA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION THEORY; ASYMPTOTIC STABILITY; COMPUTATIONAL COMPLEXITY; COMPUTATIONAL METHODS; ERROR ANALYSIS; FUNCTIONS; MATHEMATICAL MODELS; PARAMETER ESTIMATION; PROBLEM SOLVING; SIGNAL TO NOISE RATIO;

ANALYTIC MODELS;

REGRESSION ANALYSIS;

EID: 0141572295     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (11)

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4. V. Vapnik, E. Levin and Y. Cun, "Measuring the VC-dimension of a learning machine", Neural Computation, 6, pp. 851-876, 1994
5. X. Shao, V. Cherkassky and W. Li, "Measuring the VC-dimension using optimized experimental design", Neural Computation, 12, 8, pp. 1969-1986, 2000
6. V. Cherkassky, X. Shao, F. Mulier, and V. Vapnik, "Model Complexity Control for Regression using VC Generalization Bounds", IEEE Trans. on Neural Networks, Vol 10, No 5., 1075 -1089, 1999
7. V. Cherkassky, "Model Complexity Control and Statistical Learning Theory", Natural Computing: An International Journal, Kluwer, 1,1, 109-133, 2002
8. V. Cherkassky and Y. Ma, "Comparison of Model Selection for Regression", Neural Computation, Vol 15, 7 in press, 2003
9. V. Vapnik, Statistical Learning Theory, Wiley, 1998
10. W. Hardle, Applied Nonparametric Regression, Cambridge Univ. Press, 1995
11. J. Friedman, "Multivariate adaptive regression spines (with discussion)", Annals of Statistics, 19(1): pp 1-141, 1991