## Pattern RecognitionThis book considers classical and current theory and practice, of supervised, unsupervised and semi-supervised pattern recognition, to build a complete background for professionals and students of engineering. The authors, leading experts in the field of pattern recognition, have provided an up-to-date, self-contained volume encapsulating this wide spectrum of information. The very latest methods are incorporated in this edition: semi-supervised learning, combining clustering algorithms, and relevance feedback. · Thoroughly developed to include many more worked examples to give greater understanding of the various methods and techniques · Many more diagrams included--now in two color--to provide greater insight through visual presentation · Matlab code of the most common methods are given at the end of each chapter. · More Matlab code is available, together with an accompanying manual, via this site · Latest hot topics included to further the reference value of the text including non-linear dimensionality reduction techniques, relevance feedback, semi-supervised learning, spectral clustering, combining clustering algorithms. · An accompanying book with Matlab code of the most common methods and algorithms in the book, together with a descriptive summary, and solved examples including real-life data sets in imaging, and audio recognition. The companion book will be available separately or at a special packaged price (ISBN: 9780123744869). |

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### Índice

1 | |

13 | |

91 | |

Chapter 4 Nonlinear Classifiers | 151 |

Chapter 5 Feature Selection | 261 |

Data Transformation and Dimensionality Reduction | 323 |

Chapter 7 Feature Generation II | 411 |

Chapter 8 Template Matching | 481 |

Sequential Algorithms | 627 |

Hierarchical Algorithms | 653 |

Schemes Based on Function Optimization | 701 |

Chapter 15 Clustering Algorithms IV | 765 |

Chapter 16 Cluster Validity | 863 |

Appendix A Hints from Probabilityand Statistics | 915 |

Appendix B Linear Algebra Basics | 927 |

Appendix C Cost Function Optimization | 930 |

Chapter 9 ContextDependent Classification | 521 |

The Epilogue | 567 |

Basic Concepts | 595 |

Appendix D Basic Definitions from Linear Systems Theory | 946 |

949 | |