From：http://www.ph.tn.tudelft.nl/PRInfo/books.html

原来网站声明：
Due to a reorganisation we are not able anymore to maintain these files.
They will be removed in the near future.
所以保留一份备用（自己有的或者看的用了红色标注）。

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.

Pattern Recognition and Statistical Learning

Neural Networks

Machine Learning and Information Theory

Image Processing

Signal Processing

Books of Historical Interest

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.

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