Sankar K. Pal Livres

This text demonstrates how various soft computing tools can be applied to design and develop methodologies and systems with case based reasoning, that is, for real-life decision-making or recognition problems. Comprising contributions from experts, it introduces the basic concepts and theories, and includes many reports on real-life applications. This book is of interest to graduate students and researchers in computer science, electrical engineering and information technology, as well as researchers and practitioners from the fields of systems design, pattern recognition and data mining.

The volume provides a collection of twenty articles containing new material and describing, in a unified way, the basic concept and characterising features of rough set theory and its integration with fuzzy set theory, for developing an efficient soft computing strategy of machine learning. The articles, written by leading experts all over the world, demonstrates how rough-fuzzy hybridization can be made in various ways to provide flexible information processing capabilities for handling different real life ambiguous decision making problems. Application domain includes, among others, data mining, signal processing, pattern classification, feature/rule generation, knowledge based expert systems, medical information systems and neural computation. Methods of integrating rough-fuzzy hybridization with artificial neural networks for efficient knowledge encoding and network architecture design are described. The volume provides a balanced mixture of both theory and applications with extensive bibliography.

Any task that involves decision-making can benefit from soft computing techniques which allow premature decisions to be deferred. The processing and analysis of images is no exception to this rule. In the classical image analysis paradigm, the first step is nearly always some sort of segmentation process in which the image is divided into (hopefully, meaningful) parts. It was pointed out nearly 30 years ago by Prewitt (1] that the decisions involved in image segmentation could be postponed by regarding the image parts as fuzzy, rather than crisp, subsets of the image. It was also realized very early that many basic properties of and operations on image subsets could be extended to fuzzy subsets; for example, the classic paper on fuzzy sets by Zadeh [2] discussed the „set algebra“ of fuzzy sets (using sup for union and inf for intersection), and extended the defmition of convexity to fuzzy sets. These and similar ideas allowed many of the methods of image analysis to be generalized to fuzzy image parts. For are cent review on geometric description of fuzzy sets see, e. g. , [3]. Fuzzy methods are also valuable in image processing and coding, where learning processes can be important in choosing the parameters of filters, quantizers, etc.

InhaltsverzeichnisKeynote Papers.Invited Papers.Contributory Papers Clustering, Feature Selection and Learning.Classification.Neural Networks and Applications.Fuzzy Logic and Applications.Optimization and Representation.Image Processing and Analysis.Video Processing and Computer Vision.Image Retrieval and Data Mining.Bioinformatics Application.Web Intelligence and Genetic Algorithms.Rough Sets, Case-Based Reasoning and Knowledge Discovery.

Soft computing comprises various paradigms dedicated to approximately solving real-world problems, e.g.