Best harmony, unified RPCL and automated model selection for unsupervised and supervised learning on Gaussian mixtures, three-layer nets and ME-RBF-SVM models

International Journal of Neural Systems
L Xu

Abstract

After introducing the fundamentals of BYY system and harmony learning, which has been developed in past several years as a unified statistical framework for parameter learning, regularization and model selection, we systematically discuss this BYY harmony learning on systems with discrete inner-representations. First, we shown that one special case leads to unsupervised learning on Gaussian mixture. We show how harmony learning not only leads us to the EM algorithm for maximum likelihood (ML) learning and the corresponding extended KMEAN algorithms for Mahalanobis clustering with criteria for selecting the number of Gaussians or clusters, but also provides us two new regularization techniques and a unified scheme that includes the previous rival penalized competitive learning (RPCL) as well as its various variants and extensions that performs model selection automatically during parameter learning. Moreover, as a by-product, we also get a new approach for determining a set of 'supporting vectors' for Parzen window density estimation. Second, we shown that other special cases lead to three typical supervised learning models with several new results. On three layer net, we get (i) a new regularized ML learning, (ii) a new criteri...Continue Reading

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Citations

Nov 6, 2002·Neural Networks : the Official Journal of the International Neural Network Society·Lei Xu
Apr 4, 2003·Neural Networks : the Official Journal of the International Neural Network Society·Giuseppe AccianiSilvano Vergura
May 1, 2013·International Journal of Neural Systems·Álvaro HerreroEmilio Corchado
Feb 21, 2014·International Journal of Neural Systems·Rongrong Fu, Hong Wang

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