Adaptive Model Selection for Digital Linear Classifiers

Abstract

Adaptive model selection can be defined as the process thanks to which an optimal classifiers h}* is automatically selected from a function class H by using only a given set of examples z. Such a process is particularly critic when the number of examples in z is low, because it is impossible the classical splitting of z in training + test + validation. In this work we show that the joined investigation of two bounds of the prediction error of the classifier can be useful to select h}* by using z for both model selection and training. Our learning algorithm is a simple kernel-based Perceptron that can be easily implemented in a counter-based digital hardware. Experiments on two real world data sets show the validity of the proposed method.