Convolution Kernels on Discrete Structures

Convolution Kernels on Discrete Structures (1999)

by David Haussler

Citations:

281 - 0 self

User correction supplied by mph

Datum	Value	Source
TITLE	Convolution Kernels on Discrete Structures	user correction
AUTHOR NAME	David Haussler	user correction
AUTHOR AFFIL	Department of Computer Science; University of California at Santa Cruz	user correction
AUTHOR ADDR	Santa Cruz, CA 95064	user correction
ABSTRACT	We introduce a new method of constructing kernels on sets whose elements are discrete structures like strings, trees and graphs. The method can be applied iteratively to build a kernel on an infinite set from kernels involving generators of the set. The family of kernels generated generalizes the family of radial basis kernels. It can also be used to define kernels in the form of joint Gibbs probability distributions. Kernels can be built from hidden Markov random elds, generalized regular expressions, pair-HMMs, or ANOVA decompositions. Uses of the method lead to open problems involving the theory of infinitely divisible positive definite functions. Fundamentals of this theory and the theory of reproducing kernel Hilbert spaces are reviewed and applied in establishing the validity of the method.	user correction
YEAR	1999	SVM HeaderParse 0.2
CITATIONS	33 found	ParsCit 1.0