Pattern Recognition and String Matching

By: Dechang Chen (Editor), Xiuzhen Cheng (Editor)

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Hardcover | 30 November 2003

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776 Pages

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24.13 x 16.51 x 3.18

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The research and development of pattern recognition have proven to be of importance in science, technology, and human activity. Many useful concepts and tools from different disciplines have been employed in pattern recognition. Among them is string matching, which receives much theoretical and practical attention. String matching is also an important topic in combinatorial optimization. This book is devoted to recent advances in pattern recognition and string matching. It consists of twenty eight chapters written by different authors, addressing a broad range of topics such as those from classifica tion, matching, mining, feature selection, and applications. Each chapter is self-contained, and presents either novel methodological approaches or applications of existing theories and techniques. The aim, intent, and motivation for publishing this book is to pro vide a reference tool for the increasing number of readers who depend upon pattern recognition or string matching in some way. This includes students and professionals in computer science, mathematics, statistics, and electrical engineering. We wish to thank all the authors for their valuable efforts, which made this book a reality. Thanks also go to all reviewers who gave generously of their time and expertise.

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"This edited volume is concerned with pattern recognition with an evident focus on string matching treated as one of the fundamentals in the area. The collection of contributed papers is a clear testimony to the recent developments and research directions. ... Overall, the book is a comprehensive, well-focused and carefully organized compendium of recent developments in string-oriented pattern recognition - of high interest to researchers and practitioners in string-oriented pattern recognition." (Witold Pedryez, Mathematical Reviews, Issue 2005 b)

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You Can Find This Book In

Non-Fiction Computing & I.T.Computer Science Image Processing Artificial Intelligence Computer Vision Mathematics Combinatorics & Graph Theory Pattern Recognition

Foreword
Correcting the Training Data	p. 1
Context Free Grammars and Semantic Networks for Flexible Assembly Recognition	p. 43
Stochastic Recognition of Occluded Objects	p. 73
Approximate String Matching for Angular String Elements with Applications to On-line and Off-line Handwriting Recognition	p. 105
Uniform, Fast Convergence of Arbitrarily Tight Upper and Lower Bounds on the Bayes Error	p. 131
Building RBF Networks for Time Series Classification by Boosting	p. 135
Similarity Measures and Clustering of String Patterns	p. 155
Pattern Recognition for Intrusion Detection in Computer Networks	p. 195
Model-Based Pattern Recognition	p. 219
Structural Pattern Recognition in Graphs	p. 255
Deriving Pseudo-Probabilities of Correctness Given Scores (DPPS)	p. 281
Weighed Mean and Generalized Median of Strings	p. 295
A Region-Based Algorithm for Classifier-Independent Feature Selection	p. 315
Inference of K-Piecewise Testable Tree Languages	p. 341
Mining Partially Periodic Patterns With Unknown Periods From Event tream	p. 353
Combination of Classifiers for Supervised Learning: A Survey	p. 379
Image Segmentation and Pattern Recognition: A Novel Concept, the Histogram of Connected Elements	p. 411
Prototype Extraction for k-NN Classifiers using Median Strings	p. 465
Cyclic String Matching: Efficient Exact and Approximate Algorithms	p. 477
Homogeneity, Autocorrelation and Anisotropy in Patterns	p. 499
Robust Structural Indexing through Quasi-Invariant Shape Signatures and Feature Generation	p. 517
Energy Minimisation Methods for Static and Dynamic Curve Matching	p. 545
Recent Feature Selection Methods in Statistical Patterns Recognition	p. 565
Fast Image Segmentation under Noise	p. 617
Set Analysis of Coincident Errors and Its Applications for Combining Classifiers	p. 647
Enhanced Neighbourhood Specifications for Pattern Classification	p. 673
Algorithmic Synthesis in Neural Network Training for Pattern Recognition	p. 703
Binary Strings and multi-class learning problems	p. 741
Table of Contents provided by Blackwell. All Rights Reserved.

Pattern Recognition and String Matching

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