## 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. |

### What people are saying - Write a review

User Review - Flag as inappropriate

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.

User Review - Flag as inappropriate

This book is very theoretical in nature. It's not a good book for beginners unless they have a very strong background in probability, statistics and linear algebra; even then it may not be the best place to start. The Duda books is much better for people new to pattern recognition.

### 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 autocorrelation matrix Bayes classiﬁer Bayes error Bhattacharyya distance bias bound boundary branch and bound calculated Chapter class separability classiﬁcation error coefﬁcients components computed conﬁrm convergence correlation covariance matrix criterion Data I-A decision rule deﬁned density estimate density function design samples diagonal difﬁcult discriminant function discussed effect eigenvalues and eigenvectors Equation error estimate example expected value expected vector Experiment expressed feature extraction ﬁnd ﬁnding ﬁnite ﬁrst ﬁxed Fukunaga given IEEE independent iterative kernel function likelihood ratio linear classiﬁer linear transformation mean vector mean-square error method minimize misclassiﬁed node nonparametric normal distributions number of clusters number of samples obtained optimal optimum orthonormal parameters Parzen pattern recognition probability problem procedure quadratic classiﬁer random variable random vector respect sample covariance matrix sample mean satisﬁed second order selection sequence shows subset Table technique term test samples threshold tion Trans variance vector and covariance zero

### Popular passages

Page 11 - R. 0. Duda and PE Hart, Pattern Classification and Scene Analysis, Wiley, 1972.

Page 39 - Since the determinant of the product of matrices is the product of the determinants...

Page 3 - 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.