Due to a reorganisation we are not able anymore to maintain these files. They will be removed in the near future.

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Pattern Recognition Books

Below a number of monographs is listed that can be useful for students and researchers in the field of pattern recognition. A list of book announcements received by email can be found here. There is also a general entry on Scientific Publishing Companies.

Books on Pattern Recognition and (Statistical) Learning

• A. K. Suykens, G. Horvath, S. Basu, C. Micchelli, J. Vandewalle (Eds.) Advances in Learning Theory: Methods, Models and Applications, NATO Science Series III: Computer & Systems Sciences, Volume 190, IOS Press Amsterdam, 2003.
• M. I. Schlesinger, V. Hlavác, Ten Lectures on Statistical and Structural Pattern Recognition, Kluwer Academic Publishers, 2002.
• D. J. Hand, H. Mannila and P. Smyth, Principles of Data Mining, MIT Press, August 2001.
• A. Hyvärinen, J. Karhunen, and E. Oja, Independent Component Analysis, John Wiley & Sons, 2001.
• T. Hastie, R. Tibshirani, and J. Fridman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer-Verlag, 2001.
• R. O. Duda, P. E. Hart and D. G. Stork, Pattern Classification (2nd ed.), John Wiley and Sons, 2001.
• S. Raudys, Statistical and Neural Classifiers, Springer, 2001.
• G.J. McLachlan and D. Peel, Finite Mixture Models, New York: Wiley, 2000.
• M. Friedman and A. Kandel, Introduction to Pattern Recognition, statistical, structural, neural and fuzzy logic approaches, World Scientific, Signapore, 1999.
• D. J. Hand, J. N. Kok and M. R. Berthold, Advances in Intelligent Data Analysis, Springer Verlag, Berlin, 1999.
• B. Schölkopf, C. J. C. Burges, and A. J. Smola, Advances in Kernel Methods, Support Vector Learning MIT Press, Cambridge, 1999.
• S. Theodoridis, K. Koutroumbas, Pattern recognition, Academic Press, 1999.
• A. Webb, Statistical pattern recognition, Oxford University Press Inc., New York, 1999.
• M. Berthold, D. J. Hand, Intelligent Data Analysis, An Introduction, Springer-Verlag, 1999.
• V. Cherkassky and F. Mulier, Learning from data, concepts, theory and methods, John Wiley & Sons, New York, 1998.
• L. Devroye, L. Gyorfi, G.Lugosi, A Probabilistic Theory of Pattern Recognition, Springer-Verlag New York, Inc.1996.
• E. Gose, R. Johnsonbaugh, S. Jost, Pattern recognition and image analysis, Pretice Hall Inc., 1996.
• J. Schurmann, Pattern classification, a unified view of statistical and neural approaches, John Wiley & Sons, New York, 1996.
• V.N. Vapnik, The Nature of Statistical Learning Theory, Springer,1996.
• B. Ripley, Pattern Recognition and Neural Networks, Cambridge University Press, Cambridge, 1996.
• C.M. Bishop, Neural Networks for Pattern Recognition, Clarendon Press, Oxford, 1995.
• D. Paulus and J. Hornegger, Pattern Recognition and Image Processing in C++, Vieweg, Braunschweig, 1995.
• R. Schalkhoff, Pattern Recognition, statistical, structural and neural approaches, John Wiley and Sons, New York, 1992.
• G.J. McLachlan, Discriminant Analysis and Statistical Pattern Recognition, John Wiley and Sons, New York, 1992.
• B. V. Dasarathy, Nearest neighbor(nn) norms: NN pattern classification techniques, IEEE Computer Society Press, Los Alamitos, 1991.
• S.M. Weiss and C.A. Kulikowski, Computer Systems that Learn, Morgan Kaufmann, San Mateo, California, 1991.
• K. Fukunaga, Introduction to Statistical Pattern Recognition (Second Edition), Academic Press, New York, 1990.
• Y.H. Pao, Adaptive Pattern Recognition and Neural Networks, Addison Wesley, Reading, Massachusetts, 1989.
• Satoshi Watanabe, Pattern Recognition, Human and Mechanical, John Wiley & Sons, New York, 1985.
• T.Y. Young and K.S. Fu, Handbook of Pattern Recognition and Image Processing, Academic Press, Orlando, Florida, 1986.
• L. Breiman, J.H. Friedman, R.A. Olshen, and C.J. Stone, Classification and regression trees, Wadsworth, California, 1984.
• P.A. Devijver and J. Kittler, Pattern Recognition, a Statistical Approach, Prentice Hall, Englewood Cliffs, London, 1982.
• R.C. Gonzalez and M.G. Thomason, Syntactic pattern recognition - An introduction, Addison-Wesley, Reading, 1982.
• J. Sklanski and G.N. Wassel, Pattern Classifiers and Trainable Machines, Springer, New York, 1981.
• R.O. Duda and P.E. Hart, Pattern classification and scene analysis, John Wiley & Sons, New York, 1973.
  (A second edition is prepared by David Stork)

Books on Neural Networks

• P. Dayan, L.F. Abbott, Theoretical Neuroscience, Computational and Mathematical Modeling of Neural Systems , MIT Press, December 2001.
• U. Seiffert, L.C. Jain (editors), Self-Organizing Neural Networks: Recent Advances and Applications (Studies in Fuzziness and Soft Computing), Springer-Verlag, November 2001.
• W. Maass and C. M. Bishop, editors, Pulsed Neural Networks, MIT Press, Cambridge, 1999.
• S. Amari, N. Kasabov, Brain-like computing and intelligent information systems, Springer Verlag, Berlin, 1998.
• G. B. Orr, K-R. Müller (editors), Neural Networks: Tricks of the Trade, Springer-Verlag Berlin Heildeberg, 1998.
• T. Kohonen, Self-Organizing Maps, Springer, Berlin, 1995, 1997.
• C. M. Bishop, editor, Neural Networks and Machine Learning 1997 NATO Advanced Study Institute, Springer 1998.
• P. Smolensky, M. C. Mozer, and D. E. Rumelhart, Mathematical Perspectives on Neural Networks, Lawrence Erlbaum Associates, Inc. Mahwah, New Yersey, 1996.
• Y. Bengio, Neural networks for speech and sequence recognition, International Thomson Publishing, London, 1995.
• LiMin Fu, Neural Networks in Computer Intelligence, McGraw-Hill, Inc., New York, NY, 1994.
• S. Haykin, Neural Networks, A Comprehensive Foundation, Macmillan, New York, NY, 1994.
• S.Y. Kung, Digital Neural Networks, Prentice Hall, Englewood Cliffs, NJ, 1993.
• Stephen I. Gallant, Neural Network Learning and Expert systems, Massachusetts Inst. of Technology, Cambridge, Massachusetts, 1993.
• A. Cichocki and R. Unbehauen, Neural Networks for Optimization and Signal Processing, John Wiley & Sons, New York, 1993.
• C. H. Chen, L. F. Pau, P. S. P. Wang, Handbook of Pattern Recognition and Computer Vision, World Scientific, Singapore, 1993.
• B. Kosko, Neural networks for signal processing, Prentice-Hall, Englewood Cliffs, 1992.
• J.M. Zurada, Artificial Neural Systems, West Publishing, St. Paul, MN, 1992.
• B. Muller and J. Reinhardt, Neural networks, an introduction, Springer-Verlag, Berlin, 1991.
• John Hertz, Anders Krogh, and Richard G. Palmer, Introduction to the Theory of Neural Computation, Addison Wesley Publ. Comp., Redwood City ,CA, 1991.
• J. Diederich, Artificial neural networks - Concept learning, IEEE Computer Society Press, Los Alamitos, 1990.
• P.D. Wasserman, Neural Computing, theory and practice, Van Nostrand Reinhold, New York, 1989.
• I. Aleksander, Neural Computing Architectures, North Oxford Academic, London, 1989.
• S. Grossberg, The Adaptive Brain I: Cognition, Learning, Reinforcement, and Rythm, Elsevier/North Holland, Amsterdam, 1987.
• S. Grossberg, The Adaptive Brain II: Vision, Speech, Language and Motor Control, Elsevier/North Holland, Amsterdam, 1987.

Books on Machine Learning

• J. Shawe-Taylor and N. Cristianini, Kernel Methods for Pattern Analysis, Cambridge University Press, 2004.
• D.J.C. MacKay, Information Theory, Inference and Learning Algorithms, Cambridge University Press, 2003.
• B. Apolloni, D. Malchiodi and S. Gaito, Algorithmic Inference in Machine Learning, International Series on Advanced Intelligence, Vol. 5, Advanced Knowledge International, 2003.
• J.A.K. Suykens, T. Van Gestel, J. De Brabanter, B. De Moor, J. Vandewalle, Least Squares Support Vector Machines, World Scientific Pub. Co., Singapore, 2002.
• B. Schölkopf and A.J. Smola, Learning with Kernels, Support Vector Machines, Regularization, Optimization, and Beyond, MIT Press, Cambridge, 2001.
• N. Cristinanini and J. Shawe-Taylor, An Introduction to Support Vector Machines, Cambridge University Press, Cambridge, UK, 2000.
• B. Schölkopf, C.J.C. Burges and A.J. Smola (editors), Advances in Kernel Methods, Support Vector Learning, MIT Press, Cambridge, 1999.
• T.M. Mitchell, Machine learning, Mc Graw-Hill, New York, 1997.
• J.R. Quinlan, C4.5: Programs for machine learning, Morgan Kaufmann Publishers, San Mateo, California, 1993.
• B.K. Natarajan, Machine learning, Morgan Kaufmann Publ, San Mateo, CA, 1991.

Books on Signal Processing

• A. Papoulis and S.U. Pillai, Probability, Random Variables and Stochastic Processes, McGraw-Hill, 4th edition, 2002.
• P. Denbigh, System Analysis and Signal Processing, Addison-Wesley, London, 1998.
• H. J. A. M. Heijmans, J. B. T. M Roerdink, Mathematical morphology and its applications to image and signal processing, Kluwer Academic Publishers, Boston/Dordrecht/London, 1998.
• V.K. Madisetti and D.B. Williams, editors, The Digital Signal Processing Handbook, IEEE Press/CRC Press, 1997.
• D. Eberly, Ridges in Image and Data Analysis Kluwer Academic Publishers, Boston/Dordrecht/London, 1996.
• J. J. K. Ruanidh, W. J. Fitzgerald, Numerical Bayesian Methods Applied to Signal Processing, Springer Verlag, Berlin, 1996.
• G. R. Wilson, K. W. Baugh, M. D. Ladd, and R. D. Priebe, Higher-order statistical signal processing, Longman, Australia, 1995.
• A. Cichocki, R. Unbehauen, Neural Networks for Optimization and Signal Processing, John Wiley & Sons, New York, 1993.
• D. H. Johnson, D. E. Dudgeon, Array signal processing, Prentice-Hall, 1993.
• L. Rabiner, B.-H. Juang, Fundamentals of Speech Recognition Prentice-Hall, Englewood Cliffs, 1993.
• B. Kosko, Neural networks for signal processing, Prentice-Hall, Englewood Cliffs, 1992.
• J. G. Proakis, D. G. Manolakis, Digital signal processing - principles, algorithms and applications, 2nd ed., MacMillan Publ., New York, 1992.
• D.E. Dudgeon and R.M. Mersereau, Multidimensional digital signal processing, Prentice-Hall, Inc, Englewood Cliffs, 1984.
• A.V. Oppenheim, A.S. Willsky, and I.T. Young, Signals and Systems, Prentice-Hall, 1983.
• A. Papoulis, Signal Analysis, McGraw-Hill, 1977.
• R.N. Bracewell,The Fourier Transform and its Applications, McGraw-Hill, third edition, 2000,1965.

Books of Historical Interest

• K. Fukunaga, Introduction to Statistical Pattern Recognition (First Edition), Academic Press, New York, 1972.
• J.M. Mendel and K.S. Fu, Adaptive, learning, and pattern recognition systems: theory and applications, Academic Press, New York, 1970.
• M. Minsky and S. Papert, Perceptrons: An Introduction to Computational Geometry, MIT Press, Cambridge, Mass, 1969.
• A.G. Arkadev and E.M. Braverman, Teaching Computers to Recognize Patterns, Academic Press, London, 1966.
• Nilsson, N.J., Learning Machines, McGraw-Hill, New York, 1965.
• G.S. Sebestyen, Decision-Making Processes in Pattern Recognition, Macmillan, New York, 1962.
• Rosenblatt, F., Principles of Neurodynamics: Perceptrons and the theory of brain mechanisms, Spartan Books, Washington, D.C., 1962.