computer technology

Before this book was published, I considered "Pattern Recognition", by Theordoridis to be the best text for learning pattern recognition and classification. Although Theordoridis' book has some difficulties (not enough concrete exercises, ommission of structural methods, and not enough material on Bayesian Networks and HMMs), it seemed significantly better than previous texts. However, not only does Duda, Hart, and Stork's book succeed in those areas where the former fails, but it also has other strengths that the former book does not have: better illustrations, boxed formulas and algorithms, and highlighted defintions. Although somewhat superficial, these improvements mark the fact that pattern recognition is now considered a mainstream subject, and thus requires a mainstream text that keeps the integrity and rigor of the subject matter, while simultaneously making it more accessible to the average engineer. The new champ, however, does not come without it's own shortcomings. For example, I believe the last 3 chapters of Theodoridis' book should be read by anyone who wants a deeper understanding of clustering techniques for unsupervised learning. Moreover, this book fails to acknowledge the brilliant work done in computational learning by Vapnik and Chervonenkis, which reveals the authors' bias towards practice over theory. I believe it deserves more than passing mention in the historical notes section of unsupervised learning.

If you think that some method such as SVM is the "holy grail" of machine learning and pattern recognition and are interested only in an in-depth coverage of that specific tool, this book is not for you. If, however, you want to understand the basic concepts and methods employed by a broad range of researchers and scientists, I highly recommend buying it.

The book covers a broad range of topics in pattern recognition. Its explanations are lucid, and its illustrations are helpful. The book is well-written and well-organized. When using this book as part of a low-level graduate course, I was not particularly impressed. Recently, however, I have found myself frequently going back to the book to refresh my understanding of the basic idea of some topic. I recommend PC as a companion text for a course in pattern recognition. I also recommend purchasing the book for private use.

This book is full of useful algorithms, as well as the theory behind them. The explanations are good, although they sometimes require reading them several times to fully grip what is going on and why it works (but this is the case with many useful algorithms). This is a must-have for pattern rec work.