Pattern Recognition In Softcomputing Paradigm
Author:   Nikhil R Pal (Indian Statistical Inst, India)
Publisher:   World Scientific Publishing Co Pte Ltd
Volume:   2
ISBN:  

9789810244910


Pages:   412
Publication Date:   06 February 2001
Format:   Hardback
Availability:   Out of stock   Availability explained
The supplier is temporarily out of stock of this item. It will be ordered for you on backorder and shipped when it becomes available.

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Pattern Recognition In Softcomputing Paradigm


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Overview

Pattern recognition (PR) consists of three important tasks: feature analysis, clustering and classification. Image analysis can also be viewed as a PR task. Feature analysis is a very important step in designing any useful PR system because its effectiveness depends heavily on the set of features used to realize the system. A distinguishing feature of this volume is that it deals with all three aspects of PR, namely feature analysis, clustering and classifier design. It also encompasses image processing methodologies and image retrieval with subject information. The other interesting aspect of the volume is that it covers all three major facets of soft computing: fuzzy logic, neural networks and evolutionary computing.

Full Product Details

Author:   Nikhil R Pal (Indian Statistical Inst, India)
Publisher:   World Scientific Publishing Co Pte Ltd
Imprint:   World Scientific Publishing Co Pte Ltd
Volume:   2
Dimensions:   Width: 16.30cm , Height: 3.00cm , Length: 23.00cm
Weight:   0.689kg
ISBN:  

9789810244910


ISBN 10:   9810244916
Pages:   412
Publication Date:   06 February 2001
Audience:   College/higher education ,  Professional and scholarly ,  Postgraduate, Research & Scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Out of stock   Availability explained
The supplier is temporarily out of stock of this item. It will be ordered for you on backorder and shipped when it becomes available.

Table of Contents

Dimensionality reduction techniques for interactive visualization, exploratory data analysis and classification, A. Konig; feature selection by artificial neural network for pattern classification, B. Chakraborty; a new clustering with estimation of cluster number based on genetic algorithm, K. Imai et al; minimizing the measurement cost in the classification of new samples by neural-network-based classifiers, H. Ishibuchi and M. Nii; extraction of fuzzy rules from numerical data for classifiers, N.R. Pal and A. Sarkar; a texture image segmentation method using neural networks and binary features, J. Zhang and S. Oe; image retrieval system based on subjective information, K Yoshida et al. (Part contents).

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