Pattern Recognition References

The following books cover statistical pattern recognition and related topics in depth. Information available over the Web is currently rather limited, although one can find a lot of related work on neural networks, which provide an attractive way to implement pattern classifiers.
  1. P. A. Devijver and J. Kittler, Pattern Recognition: A Statistical Approach (Prentice-Hall International, Englewood Cliffs, NJ, 1980). A very clear presentation of the mathematical foundations.

  2. R. O. Duda and P. E. Hart, Pattern Classification and Scene Analysis (Wiley-Interscience, New York, 1973). Still in print after all these years.

  3. K. Fukunaga, Introduction to Statistical Pattern Recognition, 2nd Ed. (Academic Press, New York, 1990. A standard, widely-used textbook.

  4. D. J. Hand, Discrimination and Classification (John Wiley and Sons, Chichester, UK, 1981). A warmly recommended introductory book.

  5. S. Haykin, Neural Networks (MacMillan, NY, 1993). There are dozens of interesting books on neural networks. Haykin is an excellent, engineering-oriented textbook.

  6. T. Masters, Advanced Algorithms for Neural Networks (Wiley, NY, 1995). Well described by the title, with a chapter devoted to the often overlooked issue of validation.

  7. Y-H Pao, Adaptive Pattern Recognition and Neural Networks (Addison-Wesley Publishing Co., Reading, MA, 1989). Augments statistical procedures by including neural networks, fuzzy-set representations and self-organizing feature maps.

  8. R. Schalkoff, Pattern Recognition: Statistical, Structural and Neural Approaches (John Wiley & Sons, New York, 1992). A clear presentation of the essential ideas in three important approaches to pattern recognition.

  9. M. Stefik, Introduction to Knowledge Systems (Morgan Kaufmann, San Francisco, CA, 1995). Although this excellent book is not oriented towards pattern recognition per se, the methods it presents are the basis for knowledge-based pattern recognition.

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