Conference Paper

Intersection and Signed-Intersection Kernels for Intervals.

DOI: 10.3233/978-1-58603-925-7-262 Conference: Artificial Intelligence Research and Development, Proceedings of the 11th International Conference of the Catalan Association for Artificial Intelligence, CCIA 2008, October 22-24, 2008, Sant Martí d'Empúries, Spain
Source: DBLP

ABSTRACT In this paper two kernels for interval data based on the intersection operation are introduced. On the one hand, it is demonstrated that the intersection length of two intervals is a positive definite (PD) kernel. On the other hand, a signed variant of this kernel, which also permits discriminating between disjoint intervals, is demonstrated to be a conditionally positive definite (CPD) kernel. The potentiality and performance of the two kernels presented when applying them to learning machine techniques based on kernel methods are shown by considering three different examples involving interval data.

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    ABSTRACT: A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented. The technique is applicable to a wide variety of classifiaction functions, including Perceptrons, polynomials, and Radial Basis Functions. The effective number of parameters is adjusted automatically to match the complexity of the problem. The solution is expressed as a linear combination of supporting patterns. These are the subset of training patterns that are closest to the decision boundary. Bounds on the generalization performance based on the leave-one-out method and the VC-dimension are given. Experimental results on optical character recognition problems demonstrate the good generalization obtained when compared with other learning algorithms. 1


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Jun 5, 2014