Hybrid Methods In Pattern Recognition

By: Horst Bunke (Editor), Abraham Kandel (Editor)

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Hardcover | 22 May 2002

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Hardcover
336 Pages

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22.86 x 15.88 x 3.18

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The field of pattern recognition has seen enormous progress since its beginnings. A large number of different approaches have been proposed and hybrid methods aim at combining the advantages of different paradigms within a single system. This collection of articles describes progress in this emerging field. It covers topics such as the combination of neural nets with fuzzy systems or hidden Markov models, neural networks for the processing of symbolic data structures, hybrid methods in data mining, the combination of symbolic and subsymbolic learning, and others. Also included is work on multiple classifier systems. Furthermore, the work deals with applications in on-line and off-line handwriting recognition, remotely-sensed image interpretation, fingerprint identification, and automatic text categorization.

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Non-Fiction Computing & I.T.Computer Science Artificial Intelligence Neural Networks & Fuzzy Systems Computer Vision Pattern Recognition Image Processing Medicine Medicine in General Engineering & Technology Technology in General

Booktopia Publisher Services World Scientific Publishing

Preface
Neuro-Fuzzy Systems
Fuzzification of Neural Networks for Classification Problems	p. 1
Neural Networks for Structural Pattern Recognition
Adaptive Graphic Pattern Recognition: Foundations and Perspectives	p. 33
Adaptive Self-Organizing Map in the Graph Domain	p. 61
Clustering for Hybrid Systems
From Numbers to Information Granules: A Study in Unsupervised Learning and Feature Analysis	p. 75
Combining Neural Networks and Hidden Markov Models
Combination of Hidden Markov Models and Neural Networks for Hybrid Statistical Pattern Recognition	p. 113
From Character to Sentences: A Hybrid Neuro-Markovian System for On-Line Handwriting Recognition	p. 145
Multiple Classifier Systems
Multiple Classifier Combination: Lessons and Next Steps	p. 171
Design of Multiple Classifier Systems	p. 199
Fusing Neural Networks Through Fuzzy Integration	p. 227
Applications of Hybrid Systems
Hybrid Data Mining Methods in Image Processing	p. 253
Robust Fingerprint Identification Based on Hybrid Pattern Recognition Methods	p. 275
Text Categorization Using Learned Document Features	p. 301
Table of Contents provided by Blackwell. All Rights Reserved.

Hybrid Methods In Pattern Recognition

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Hardcover

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