Piecewise linear classifiers preserving high local recognition rates

summary:We propose a new method to construct piecewise linear classifiers. This method constructs hyperplanes of a piecewise linear classifier so as to keep the correct recognition rate over a threshold for a training set. The threshold is determined automatically by the MDL (Minimum Description Length) criterion so as to avoid overfitting of the classifier to the training set. The proposed method showed better results in some experiments than a previous method

MDL-Based Selection of the Number of Components in Mixture Models for Pattern Classification

. A new method is proposed for selection of the optimal number of components of a mixture model for pattern classification. We approximate a class-conditional density by a mixture of Gaussian components. We estimate the parameters of the mixture components by the EM (Expectation Maximization) algorithm and select the optimal number of components on the basis of the MDL (Minimum Description Length) principle. We evaluate the goodness of an estimated model in a tradeoff between the number of the misclassified training samples and the complexity of the model. 1 Introduction In pattern recognition, we often apply clustering techniques over the training samples to understand the spatial structure of the samples and to reduce the computational costs of design of classifiers. In approximating a class-conditional density by a mixture of Gaussian components, we encounter the situation of finding initial components for an iterative procedure called EM algorithm [1]. In this situation, two probl..

Signal Learning with Messages by Reinforcement Learning in Multi-agent Pursuit Problem

AbstractCommunication is a key for facilitating multi-agent coordination on cooperative problems. Reinforcement learning is one of the learning methods for such cooperative behavior of agents. Kasai et al. proposed Signal Learning (SL) and Signal Learning with Messages (SLM) by which agents learn policies of communication and action concurrently in multi-agent reinforcement learning framework. In this study, we experimented that the performance of the SLM is better than SL to pursuit problem where agents can observe only partial information and can move four directions. As a result, it has been shown that learning performance in SLM with longer messages is better than SL

A Subclass-Based Mixture Model for Pattern Recognition

A classifier based on a mixture model is proposed. The EM algorithm for construction of a mixture density is sensitive to the initial densities. It is also difficult to determine the optimal number of component densities. In this study, we construct a mixture density on the basis of a hyperrectangles found in the subclass method, in which the number of components is determined automatically. Experimental results show the effectiveness of this approach. 1. Introduction Many nonlinear classifiers have been studied in several ways: approximations of a class-conditional density, nearest neighbor approaches, piecewise linear classifiers, and neural networks. Among these, the approximation approach is strongly related to Bayes rule and is promising for pattern recognition. The approaches based on an approximation of a class-conditional density are divided into two categories: 1) approximations by kernel functions over the training samples, and 2) approximations by a mixture of basis function..