Does anyone know any good intro pattern matching books. We are starting a study group on pattern matching...right now we are using Pattern Classification by Richard O. Duda. The book is ok, but I wish they would elaborate a bit more and give more examples. I want something that has math mixed with a lot of explanation. We are not looking to study one topic, but get a good grasp of a bunch of techniques. By the way I have 'Artificial Intelligence: A Modern Approach'. It is ok, but missing the mathematical depth that people in the group want to do. I perfer the software implementation side, but the group wants to work a lot of math problems. Any suggestions on software book would be helpful as well.

I dont know if thats gonna help you,
but there is the journal 'Pattern Recognition', which you should be able to find in any university library, and you can also look into the proceedings of the ICPR conference. Both are not introductory level, but you'll find all of the maths you want.

While I think of it, I never heard of a book on PM / PR before I read your post. Must not be a lot of them.

Sorry if Im not more helpful.

Neural Networks for Pattern Recognition by Bishop may be helpful. Take a look.

Introduction to the theory of computation by Michael Sipser is an essential, but hard, prerequisite for pattern matching.

'Neural Networks for Pattern Recognition by Bishop' - I have heard of this one and it has good reviews, but I already have another good intro to neural nets.
We are looking for some text that either covers a bunch of areas, or more than one text that covers other areas like Bayesian and vector machines?

'Pattern Recognition' - Saw some references to this, but not sure it will be helpful. Looking for something a bit more introductory.....introductory but doesn't gloss over the math with the wave of a hand.

Introduction to the theory of computation - Actually never heard of this one, but it looks interesting. I have already covered most of the topics in it in someway so don't think it will be too helpful for this group.

How about The Elements of Statistical Learning by T. Hastie, R. Tibshirani, and J.H. Friedman?? Similar to Duda but with more theoretical depth.

-Kirk