Pattern Recognition, 3/e(IE)
暫譯: 模式識別，第3版(IE)

A classic -- offering comprehensive and unified coverage with a balance between theory and practice! Pattern recognition is integral to a wide spectrum of scientific disciplines and technologies including image analysis, speech recognition and audio classification, communications, computer-aided diagnosis, data mining. The authors, leading experts in the field of pattern recognition, have once again provided an up-to-date, self-contained volume encapsulating this wide spectrum of information. Each chapter is designed to begin with basics of theory progressing to advanced topics and then discusses cutting-edge techniques. Problems and exercises are present at the end of each chapter with a solutions manual provided via a companion website where a number of demonstrations are also available to aid the reader in gaining practical experience with the theories and associated algorithms. This edition includes discussion of Bayesian classification, Bayesian networks, linear and nonlinear classifier design (including neural networks and support vector machines), dynamic programming and hidden Markov models for sequential data, feature generation (including wavelets, principal component analysis, independent component analysis and fractals), feature selection techniques, basic concepts from learning theory, and clustering concepts and algorithms. This book considers classical and current theory and practice, of both supervised and unsupervised pattern recognition, to build a complete background for professionals and students of engineering. FOR INSTRUCTORS: To obtain access to the solutions manual for this title simply register on our textbook website (textbooks.elsevier.com)and request access to the Computer Science or Electronics and Electrical Engineering subject area. Once approved (usually within one business day) you will be able to access all of the instructor-only materials through the "Instructor Manual" link on this book's full web page.

 

Table of Contents

Chapter 1: Introduction Chapter 2: Classifiers Based on Bayes Decision Theory Chapter 3: Linear Classifiers Chapter 4: Nonlinear Classifiers Chapter 5: Feature Selection Chapter 6: Feature Generation I Chapter 7: Feature Generation II Chapter 8: Template Matching Chapter 9: Context-Dependant Classification Chapter 10: System Evaluation Chapter 11: Clustering: Basic Concepts Chapter 12: Clutering Algorithms I (Sequential) Chapter 13: Clustering Algorithms II (Hierarchical Chapter 14: Clustering Algorithms III (Functional Optimization) Chapter 15: Clustering Algorithms IV (Graph Theory) Chapter 16: Cluster Validity