A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
The nature of statistical learning theory
The nature of statistical learning theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
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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.