模式識(shí)別與神經(jīng)網(wǎng)絡(luò)

前言

Pattern recognition has a long and respectable history within engineer-ing, especially for military applications, but the cost of the hardwareboth to acquire the data （signals and images） and to compute theanswers made it for many years a rather specialist subject. Hardwareadvances have made the concerns of pattern recognition of much widerapplicability. In essence it covers the following problem:'Given some examples of complex signals and the correctdecisions for them, make decisions automatically for a streamof future examples.'There are many examples from everyday life:Name the species of a flowering plant.Grade bacon rashers from a visual image.Classify an X-ray image of a tumour as cancerous or benign.Decide to buy or sell a stock option.Give or refuse credit to a shopper.Many of these are currently performed by human experts, but it isincreasingly becoming feasible to design automated systems to replacethe expert and either perform better （as in credit scoring） or 'clone' theexpert （as in aids to medical diagnosis）.Neural networks have arisen from analogies with models of the waythat humans might approach pattern recognition tasks, although theyhave developed a long way from the biological roots. Great claims havebeen made for these procedures, and although few of these claims havewithstood careful scrutiny, neural network methods have had greatimpact on pattern recognition practice. A theoretical understanding ofhow they work is still under construction, and is attempted here byviewing neural networks within a statistical framework, together withmethods developed in the field of machine learning.One of the aims of this book is to be a reference resource, so almostall the results used are proved （and the remainder are given referencesto complete proofs）. The proofs are often original.

內(nèi)容概要

本書是模式識(shí)別和神經(jīng)網(wǎng)絡(luò)方面的名著，講述了模式識(shí)別所涉及的統(tǒng)計(jì)方法、神經(jīng)網(wǎng)絡(luò)和機(jī)器學(xué)習(xí)等分支。書的內(nèi)容從介紹和例子開始，主要涵蓋統(tǒng)計(jì)決策理論、線性判別分析、彈性判別分析、前饋神經(jīng)網(wǎng)絡(luò)、非參數(shù)方法、樹結(jié)構(gòu)分類、信念網(wǎng)、無(wú)監(jiān)管方法、探尋優(yōu)良的模式特性等方面的內(nèi)容?！　”緯勺鳛榻y(tǒng)計(jì)與理工科研究生課程的教材，對(duì)模式識(shí)別和神經(jīng)網(wǎng)絡(luò)領(lǐng)域的研究人員也是極有價(jià)值的參考書。

作者簡(jiǎn)介

里普利（B.D.Ripley）著名的統(tǒng)計(jì)學(xué)家，牛津大學(xué)應(yīng)用統(tǒng)計(jì)教授。他在空間統(tǒng)計(jì)學(xué)、模式識(shí)別領(lǐng)域作出了重要貢獻(xiàn)，對(duì)S的開發(fā)以及S-PLUSUS和R的推廣應(yīng)用有著重要影響。20世紀(jì)90年代他出版了人工神經(jīng)網(wǎng)絡(luò)方面的著作，影響很大，引導(dǎo)統(tǒng)計(jì)學(xué)者開始關(guān)注機(jī)器學(xué)習(xí)和數(shù)據(jù)挖掘。除本書外，他還著有Modern Applied Statistics with S和S Programming。

書籍目錄

1　Introduction and Examples　1.1　How do neural methods differ?　1.2　The patterm recognition task　1.3　Overview of the remaining chapters　1.4　Examples　1.5　Literature2　Statistical Decision Theory　2.1　Bayes rules for known distributions　2.2　Parametric models　2.3　Logistic discrimination　2.4　Predictive classification　2.5　Alternative estimation procedures　2.6　How complex a model do we need?　2.7　Performance assessment　2.8　Computational learning approaches3　Linear Discriminant Analysis　3.1　Classical linear discriminatio　3.2　Linear discriminants via regression　3.3　Robustness　3.4　Shrinkage methods　3.5　Logistic discrimination　3.6　Linear separatio andperceptrons4　Flexible Diseriminants　4.1　Fitting smooth parametric functions　4.2　Radial basis functions　4.3　Regularization5　Feed-forward Neural Networks　5.1　Biological motivation　5.2　Theory　5.3　Learning algorithms　5.4　Examples　5.5　Bayesian perspectives　5.6　Network complexity　5.7　Approximation results6　Non-parametric Methods　6.1　Non-parametric estlmation of class densities　6.2　Nearest neighbour methods　6 3　Learning vector quantization　6.4　Mixture representations7　Tree-structured Classifiers　7.1　Splitting rules　7.2　Pruning rules　7.3　Missing values　7.4　Earlier approaches　7.5　Refinements　7.6　Relationships to neural networks　7.7　Bayesian trees8　Belief Networks　8.1　Graphical models and networks　8.2　Causal networks　8 3　Learning the network structure　8.4　Boltzmann machines　8.5　Hierarchical mixtures of experts9　Unsupervised Methods　……10　Finding Good Pattern FeaturesA　Statistical SidelinesGlossaryReferencesAuthor IndexSubject Index

章節(jié)摘錄

插圖：The calculations here are from Hjort （1986）; versions of these for-mulae are given by Aitchison & Dunsmore （1975） （up to the differencesin the meaning of their multivariate t） and Geisser （1993）. This ap-proach is originally due to Geisser （1964, 1966）.The differences between the predictive and plug-in approaches willbe small or zero for roughly equally prevalent classes. In other cases,for example screening for rare diseases or when very few data areavailable, the differences can be dramatic as shown by the examples inAitchison & Dunsmore （1975, 11.5-11.6）. The latter do have groupswith nk only slightly greater than p, for example p = 8 and n2 = 11when fitting a covariance matrix to each class, which would be seenas over-fitting in the plug-in approach. （Indeed, one might choose notto use all the variables, or perhaps to restrict the class of covariancematrices considered.）Aitchison et al. （1977） conducted a small-sample simulation compar-ison of the plug-in and predictive methods for two multivariate normalpopulations. They were （correctly） criticized by Moran & Murphy（1979） for using the accuracy of the estimation of the log=odds as thebasis of comparison rather than error rates, and for including mainlyequal sample sizes of the two classes. Moran & Murphy's results showvery little difference in the error rates, and show that for estimationof the log-odds the debiasing methods of Section 2.5 are effective inremoving the dramatic optimism of the plug-in method where it occurs.

媒體關(guān)注與評(píng)論

“……模式分類和神經(jīng)網(wǎng)絡(luò)技術(shù)應(yīng)用A-面的優(yōu)秀教材……Ripley寫了一本詳盡、易懂的教材……這本書用簡(jiǎn)明的形式和迷人的風(fēng)格介紹了統(tǒng)計(jì)模式識(shí)別和神經(jīng)網(wǎng)絡(luò)的數(shù)學(xué)理論，必將在該領(lǐng)域中廣為流傳?！薄　　蹲匀弧贰斑@本書特別值得關(guān)注，是理論與實(shí)例的完美結(jié)合?！薄　　狝.Gelman.國(guó)際統(tǒng)計(jì)學(xué)會(huì)雜志“我極力推薦這本書，任何一位研究人員都可以領(lǐng)教Ripley的博學(xué)多才，并從書中給出的大量參考文獻(xiàn)中獲益匪淺?！薄　　狣eeDenteneer。ITWNieuws“對(duì)統(tǒng)計(jì)數(shù)據(jù)分析的原理與方法感興趣的任何人都會(huì)從中受益……為未來(lái)數(shù)年的理論發(fā)展指明了方向?！薄　　猄tephenRoberts.《泰晤士報(bào)高等教育增刊》

編輯推薦

隨著人工智能、信息檢索和海量數(shù)據(jù)處理等技術(shù)的發(fā)展，模式識(shí)別成為了研究熱點(diǎn)。在《模式識(shí)別與神經(jīng)網(wǎng)絡(luò)(英文版)》中，Riprley將模式識(shí)別領(lǐng)域中的統(tǒng)計(jì)方法和基于神經(jīng)網(wǎng)絡(luò)的機(jī)器學(xué)習(xí)這兩個(gè)關(guān)鍵思想結(jié)令起來(lái)：以統(tǒng)計(jì)決策理；侖和計(jì)算學(xué)習(xí)理論為依據(jù)，建立了神經(jīng)網(wǎng)絡(luò)理論的堅(jiān)實(shí)基礎(chǔ)。在理論層面，《模式識(shí)別與神經(jīng)網(wǎng)絡(luò)(英文版)》強(qiáng)調(diào)概率與統(tǒng)計(jì)；在實(shí)踐層面。則強(qiáng)調(diào)模式識(shí)別的實(shí)用方法?！赌Ｊ阶R(shí)別與神經(jīng)網(wǎng)絡(luò)(英文版)》已被國(guó)際知名大學(xué)采用為教材，對(duì)于研究模式識(shí)別和神經(jīng)網(wǎng)絡(luò)的專業(yè)人士，也是不可不讀的優(yōu)秀參考書。

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