I just found a good book. "Pattern Recognition, 4th ed." by Theodoridis and Koutroumbas. Authors are at the University of Athens and the National Observatory of Athens.The text begins with Bayes classifiers, then covers linear classifiers, nonlinear classifiers, feature generation and selection, and ends with a large section on clustering. Clear writing and Matlab code at the ends of chapters. Introductory level but with a very wide coverage of algorithms.

A companion book has some more Matlab code and examples. The code examples will help you understand the algorithms. More code at the website, too.

My obligatory warning– please learn learn about linear regression methods at the level of chapters 1-12 of Neter et al. "Applied Linear Statistical Models, 4th ed." before you tackle any of these books. Neter will teach you the loop and matrix forms of the algorithms for regression, plus the basic theories, all of which will make any pattern recognition book much easier to understand, including this one.

P.S. the 2nd edition of Hastie et al.'s "The Elements of Statistical Learning" is now available for free as a pdf.

P.P.S. A third good book is Bishop's "Pattern Recognition and Machine Learning." Between these three books you should be able to find something useful for a pattern recognition problem.


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