## Introduction to Statistical Pattern Recognition (Google eBook)This completely revised second edition presents an introduction to statistical pattern recognition. Pattern recognition in general covers a wide range of problems: it is applied to engineering problems, such as character readers and wave form analysis as well as to brain modeling in biology and psychology. Statistical decision and estimation, which are the main subjects of this book, are regarded as fundamental to the study of pattern recognition. This book is appropriate as a text for introductory courses in pattern recognition and as a reference book for workers in the field. Each chapter contains computer projects as well as exercises. |

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I think this book is very useful for people with some statistical background knowledge (maybe not too much), and it contains a lot of basic ideas of many engineering problems, though some of them might need an upgrade for nowadays.

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### Contents

1 | |

11 | |

51 | |

Parametric Classifiers | 124 |

Parameter Estimation | 181 |

Nonparametric Density Estimation | 254 |

Nonparametric Classification and Error Estimation | 300 |

Successive Parameter Estimation | 367 |

Feature Extraction and Linear Mapping for Signal Representation | 399 |

Feature Extraction and Linear Mapping for Classification | 441 |

Clustering | 508 |

Backmatter | 564 |

598 | |

### Common terms and phrases

algorithm assume Bayes classifier Bayes error Bhattacharyya distance bias bound boundary branch and bound calculated Chapter class separability classifier design components computed convergence correlation covariance matrix criterion Data I-I decision rule density estimate density function design samples determined diagonal discriminant function discussed effect eigenvalues and eigenvectors Equation error estimate example expected value expected vector Experiment expressed feature extraction finite Fukunaga given IEEE independent iterative kernel function kNN approach likelihood ratio linear classifier linear transformation mean vector mean-square error method minimize misclassified node nonparametric normal distributions number of clusters number of samples observation obtained one-dimensional optimal optimum orthonormal orthonormal transformation parameters pattern recognition piecewise probability procedure quadratic classifier random variable random vector respect sample covariance matrix sample mean satisfy second order selection sequence shown in Fig shows subset Table technique test samples threshold tion Trans uniform kernel variance vector and covariance zero

### Popular passages

Page 12 - R. 0. Duda and PE Hart, Pattern Classification and Scene Analysis, Wiley, 1972.

Page 40 - Since the determinant of the product of matrices is the product of the determinants...

Page 4 - Thus, pattern recognition, or decision-making in a broader sense, may be considered as a problem of estimating density functions in a high-dimensional space and dividing the space into the regions of categories or classes.