Essentials of pattern recognition: an accessible approach/ Jianxin Wu

Includes bibliographical references and index

Part I Introduction and overview -- 1. Introduction -- 2. Mathematical background -- 3. Overview of a pattern recognition system -- 4. Evaluation -- Part II Domain-Independent feature extraction -- 5. Principal component analysis -- 6. Fisher's linear discriminant -- Part III Classifiers and tools -- 7. Support vector machines -- 8. Probabilistic methods -- 9. Distance metrics and data transformations -- 10. Information theory and decision trees -- Part IV Handling diverse data formats -- 11. Sparse and misaligned data -- 12. Hidden Markov model -- Part V Advanced topics -- 13. The Normal distribution -- 14. The Basic idea behind expectation-maximization -- 15. Convolutional neural networks