Online Learning for Hierarchical Networks of Locally Arranged Models using a Support Vector Domain Model

Abstract

We propose two new developments for our supervised local linear approximation technique, the so called Hierarchical Network of Locally Arranged Models. A new model will be presented that defines those local regions of the input space in which linear models are trained to approximate the target function. This model is based on a one-class support vector machine and helps to improve the approximation quality. Secondly, an online learning algorithm for our approach will be described that can be used in applications where training data is only available as a continuous stream of samples. It allows to adapted a network to a function that may change over time. The success of these two developments is proven with three benchmark tests.