Fuzzy ARTMAP with input relevances

Document Type

Article

Department or Administrative Unit

Computer Science

Publication Date

7-2006

Abstract

We introduce a new fuzzy ARTMAP (FAM) neural network: Fuzzy ARTMAP with relevance factor (FAMR). The FAMR architecture is able to incrementally "grow" and to sequentially accommodate input-output sample pairs. Each training pair has a relevance factor assigned to it, proportional to the importance of that pair during the learning phase. The relevance factors are user-defined or computed. The FAMR can be trained as a classifier and, at the same time, as a nonparametric estimator of the probability that an input belongs to a given class. The FAMR probability estimation converges almost surely and in the mean square to the posterior probability. Our theoretical results also characterize the convergence rate of the approximation. Using a relevance factor adds more flexibility to the training phase, allowing ranking of sample pairs according to the confidence we have in the information source. We analyze the FAMR capability for mapping noisy functions when training data originates from multiple sources with known levels of noise.

Comments

This article was originally published in IEEE Transactions on Neural Networks. The full-text article from the publisher can be found here.

Due to copyright restrictions, this article is not available for free download from ScholarWorks @ CWU.

Journal

IEEE Transactions on Neural Networks

Rights

© 2006 IEEE

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