## Statistical Pattern RecognitionStatistical pattern recognition is a very active area of study and research, which has seen many advances in recent years. New and emerging applications - such as data mining, web searching, multimedia data retrieval, face recognition, and cursive handwriting recognition - require robust and efficient pattern recognition techniques. Statistical decision making and estimation are regarded as fundamental to the study of pattern recognition.
For further information on the techniques and applications discussed in this book please visit www.statistical-pattern-recognition.net |

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### Contents

1 | |

2 Density estimation parametric | 33 |

3 Density estimation nonparametric | 81 |

4 Linear discriminant analysis | 123 |

5 Nonlinear discriminant analysis kernel methods | 169 |

6 Nonlinear discriminant analysis projection methods | 203 |

7 Treebased methods | 225 |

8 Performance | 251 |

11 Additional topics | 409 |

A Measures of dissimilarity | 419 |

B Parameter estimation | 431 |

C Linear algebra | 437 |

D Data | 443 |

E Probability theory | 449 |

459 | |

491 | |