Title: Accelerating Kernel Classifiers Through Borders Mapping,
Abstract: Support vector machines (SVM) and other kernel techniques represent a family
of powerful statistical classification methods with high accuracy and broad
applicability. Because they use all or a significant portion of the training
data, however, they can be slow, especially for large problems. Piecewise
linear classifiers are similarly versatile, yet have the additional advantages
of simplicity, ease of interpretation and, if the number of component linear
classifiers is not too large, speed. Here we show how a simple, piecewise
linear classifier can be trained from a kernel-based classifier in order to
improve the classification speed. The method works by finding the root of the
difference in conditional probabilities between pairs of opposite classes to
build up a representation of the decision boundary. When tested on 17 different
datasets, it succeeded in improving the classification speed of a SVM for 9 of
them by factors as high as 88 times or more. The method is best suited to
problems with continuum features data and smooth probability functions. Because
the component linear classifiers are built up individually from an existing
classifier, rather than through a simultaneous optimization procedure, the
classifier is also fast to train.