By Andrew R. Webb
Statistical trend reputation is a really lively sector of research and study, which has noticeable many advances lately. New and rising purposes - corresponding to information mining, internet looking out, multimedia information retrieval, face attractiveness, and cursive handwriting attractiveness - require powerful and effective development popularity ideas. Statistical determination making and estimation are considered as primary to the learn of development recognition.
Statistical trend attractiveness, moment version has been totally up to date with new tools, functions and references. It presents a complete advent to this bright sector - with fabric drawn from engineering, facts, desktop technological know-how and the social sciences - and covers many program components, comparable to database layout, man made neural networks, and choice aid systems.
* presents a self-contained creation to statistical trend recognition.
* every one strategy defined is illustrated by way of genuine examples.
* Covers Bayesian equipment, neural networks, aid vector machines, and unsupervised classification.
* every one part concludes with an outline of the purposes which were addressed and with extra advancements of the theory.
* comprises history fabric on dissimilarity, parameter estimation, info, linear algebra and probability.
* incorporates a number of workouts, from 'open-book' inquiries to extra long projects.
The e-book is aimed basically at senior undergraduate and graduate scholars learning statistical development attractiveness, development processing, neural networks, and knowledge mining, in either statistics and engineering departments. it's also an exceptional resource of reference for technical pros operating in complicated details improvement environments.
For extra info at the innovations and functions mentioned during this ebook please visit www.statistical-pattern-recognition.net
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Extra info for Statistical Pattern Recognition (2nd Edition)
Given a vector Y i of such samples, then the vector U 1=2 Y i C µ has the required distribution, where U is the matrix of eigenvectors of the covariance matrix and 1=2 is the diagonal matrix whose diagonal elements are the square roots of the corresponding eigenvalues (see Appendix C). 1. x µi / ¦ with means µ1 and µ2 and equal covariance matrices, 1 D 2 D . Show that the logarithm of the likelihood ratio is linear in the feature vector x. What is the equation of the decision boundary? 2. Determine the equation of the decision boundary for the more general case of 1 D Þ 2 , for scalar Þ (normally distributed classes as in Exercise 1).
Recently, there have been several books that describe the developments in pattern recognition that have taken place over the last decade, particularly the ‘neural network’ aspects, relating these to the more traditional methods. A comprehensive treatment of neural networks is provided by Haykin (1994). Bishop (1995) provides an excellent introduction to neural network methods from a statistical pattern recognition perspective. Ripley’s (1996) account provides a thorough description of pattern recognition from within a statistical framework.
However, we are limited by a finite number of training samples and also, once we start to consider parametric forms for the i , we lose the simplicity and ease of computation of the linear functions. 6 Multiple regression Many of the techniques and procedures described within this book are also relevant to problems in regression, the process of investigating the relationship between a dependent (or response) variable Y and independent (or predictor) variables X 1 ; : : : ; X p ; a regression function expresses the expected value of Y in terms of X 1 ; : : : ; X p and model parameters.
Statistical Pattern Recognition (2nd Edition) by Andrew R. Webb