- ホーム
- > 洋書
- > 英文書
- > Science / Mathematics
基本説明
Contents: Correcting the Training Data; Context Free Grammars and Semantic Networks for Flexible Assembly Recognition; Stochastic Recognition of Occluded Objects; and more.
Full Description
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.
Contents
Correcting the Training Data.- Context Free Grammars and Semantic Networks for Flexible Assembly Recognition.- Stochastic Recognition of Occluded Objects.- Approximate String Matching for Angular String Elements with Applications to On-line and Off-line Handwriting Recognition.- Uniform, Fast Convergence of Arbitrarily Tight Upper and Lower Bounds on the Bayes Error.- Building RBF Networks for Time Series Classification by Boosting.- Similarity Measures and Clustering of String Patterns.- Pattern Recognition for Intrusion Detection in Computer Networks.- Model-Based Pattern Recognition.- Structural Pattern Recognition in Graphs.- Deriving Pseudo-Probabilities of Correctness Given Scores (DPPS).- Weighed Mean and Generalized Median of Strings.- A Region-Based Algorithm for Classifier-Independent Feature Selection.- Inference of K-Piecewise Testable Tree Languages.- Mining Partially Periodic Patterns With Unknown Periods From Event tream.- Combination of Classifiers for Supervised Learning: A Survey.- Image Segmentation and Pattern Recognition: A Novel Concept, the Histogram of Connected Elements.- Prototype Extraction for k-NN Classifiers using Median Strings.- Cyclic String Matching: Efficient Exact and Approximate Algorithms.- Homogeneity, Autocorrelation and Anisotropy in Patterns.- Robust Structural Indexing through Quasi-Invariant Shape Signatures and Feature Generation.- Energy Minimisation Methods for Static and Dynamic Curve Matching.- Recent Feature Selection Methods in Statistical Pattern Recognition.- Fast Image Segmentation under Noise.- Set Analysis of Coincident Errors and Its Applications for Combining Classifiers.- Enhanced Neighbourhood Specifications for Pattern Classification.- Algorithmic Synthesis in Neural Network Training for Pattern Recognition.-Binary Strings and multi-class learning problems.
