: Training of feed-forward networks is sensitive for learning-parameters, network architecture, etc. So if a feed-forward network fails to solve a pattern classification problem one never knows if the cause of the failure is in the network or in the problem itself. Hence a well-founded indication

We examine the performance of a fuzzy genetics-based machine learning method for multidimensional pattern classification problems with continuous attributes. In our method, each fuzzy if–then rule is handled as an individual, and a fitness value is assigned to each rule. Thus, our method can

ability. The necessity of a heuristic specification method of antecedent fuzzy sets is also demonstrated by computational experiments on high-dimensional problems. Finally we examine the generalization ability of fuzzy rule-based classification systems designed by our hybrid algorithm. Index Terms

In recent years considerable interests have been given to the pattern classi-fication problem. This problem includes three main aspects, the engineering aspect, the artificial intelligence aspect and the analytical aspect (c. f. [4]). The analytical one is concerned with the mathematical techniques

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We propose a new genetic fuzzy discretization method with feature selection for the pattern classification problems. Traditional discretization methods categorize a continuous attribute into a number of bins. Because they are made on crisp discretization, there exists considerable information

This paper presents a theoretically very simple, yet efficient, multiresolution approach to gray-scale and rotation invariant texture classification based on local binary patterns and nonparametric discrimination of sample and prototype distributions. The method is based on recognizing

The primary purpose of this paper is the improved detection of a nonstationary target signal embedded in a nonstationary background. Accordingly, the first part of the paper is devoted to a detailed exposition of how to deal with the issue of nonstationarity. The material presented here starts with Love's probabilistic theory of nonstationary processes. From this principled discussion, three important tools emerge: the dynamic spectrum, the Wigner-Ville distribution as an instantaneous estimate of the dynamic spectrum, and the Love spectrum. Procedures for the estimation of these spectra are described, and their applications are demonstrated using real-life radar data. Time, an essential dimension of learning, appears explicitly in the dynamic spectrum and Wigner-Ville distribution and implicitly in the Love spectrum. In each case, the one-dimensional time series is transformed into a two-dimensional image where the presence of nonstationarity is displayed in a more visible manner tha...

The purpose of this paper is to study the problem of pattern classification as this is presented in the context of data mining. Among the various approaches we focus on the use of Fuzzy Logic for pattern classification, due to its close relation to human thinking. More specifically, this paper

Abstract not found