MLP-BASED SOURCE SEPARATION FOR MLP-LIKE NONLINEAR MIXTURES

In this paper, the nonlinear blind source separation problem is addressed by using a multilayer perceptron (MLP) as separating system, which is justified in the universal approximation property of MLP networks. An adaptive learning algorithm for a perceptron with two hidden-layers is presented. The algorithm minimizes the mutual information between the outputs of the MLP. The performance of the proposed method is illustrated by some experiments. 1. INTRODUCTION. Blind Source Separation (BSS) is a fundamental problem in signal processing. It consists of retrieving unobserved sources s1(t),..., sN (t), assumed to be statistically independent (which is phisically plausible when the source

Using online linear classifiers to filter spam Emails

The performance of two online linear classifiers - the Perceptron and Littlestone’s Winnow – is explored for two anti-spam filtering benchmark corpora - PU1 and Ling-Spam. We study the performance for varying numbers of features, along with three different feature selection methods: Information Gain (IG), Document Frequency (DF) and Odds Ratio. The size of the training set and the number of training iterations are also investigated for both classifiers. The experimental results show that both the Perceptron and Winnow perform much better when using IG or DF than using Odds Ratio. It is further demonstrated that when using IG or DF, the classifiers are insensitive to the number of features and the number of training iterations, and not greatly sensitive to the size of training set. Winnow is shown to slightly outperform the Perceptron. It is also demonstrated that both of these online classifiers perform much better than a standard Naïve Bayes method. The theoretical and implementation computational complexity of these two classifiers are very low, and they are very easily adaptively updated. They outperform most of the published results, while being significantly easier to train and adapt. The analysis and promising experimental results indicate that the Perceptron and Winnow are two very competitive classifiers for anti-spam filtering