Learning bounds for kernel regression using effective data dimensionality

Neural Computation

Volumn 17, Issue 9, 2005, Pages 2077-2098

  Zhang, Tong ^a  

^a IBM T J WATSON RESEARCH CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; ARTIFICIAL INTELLIGENCE; SIGNAL PROCESSING; THEORETICAL MODEL;

ARTIFICIAL INTELLIGENCE; MODELS, THEORETICAL; SIGNAL PROCESSING, COMPUTER-ASSISTED;

EID: 22944490838     PISSN: 08997667     EISSN: None     Source Type: Journal    
DOI: 10.1162/0899766054323008     Document Type: Article

References (14)

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11. Yurinsky, V. (1995). Sums and gaussian vectors. Berlin: Springer-Verlag.
12. Zhang, T. (2002). Covering number bounds of certain regularized linear function classes. Journal of Machine Learning Research, 2, 527-550.
13. Zhang, T. (2003a). Effective dimension and generalization of kernel learning. In S. Becker, S. Thrun, & K. Obermayer (Eds.), Neural information processing systems, 15. Cambridge, MA: MIT Press.
14. Zhang, T. (2003b). Leave-one-out bounds for kernel methods. Neural Computation, 15, 1397-1437.