Рекомендуемая литература

1. Вирт Н. Алгоритмы, структуры данных программы. М., 1985. 406 с.
2. Грис Д. Наука программирования. М., 1984. 416 с.
3. Кнут Д. Искусство программирования для ЭВМ. Т.1 Основные алгоритмы М., 1976. 734 с.
4. Липски П. Комбинаторика в программировании. М., 1988. 213 с.
5. Дейкстра Э. Дисциплина программирования. М., 1982. 274 с.
6. Дмитриева М.В., Кубенский А.А. Элементы современного программирования. СПб. 1991. 272 с.
7. Бородич и др. Паскаль для персональных компьютеров. Минск. 1991. 365 с.
8. Епанешников А., Епанешников В. Программирование в среде Turbo Pascal 7.0. М., 1993. 282 с.
9. Дмитриева М.В., Кубенский А.А. Турбо Паскаль и Турво Си: построение и обработка структур данных. СПб. 1996. 192 с.
10. Дарахвелидзе П. Марков Е. Delphi - среда визуального программирования СПб 1996 351с
11. Дмитриева М.В. Объектно-ориентированное программирование СПб 1998

Pattern Recognition Books and Links

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.

1.Pattern Recognition and Statistical Learning

2.Neural Networks

3.Machine Learning and Information Theory

4.Image Processing

5.Signal Processing

6.Books of Historical Interest

Links on NeuralNets

1. http://alife.narod.ru/ - сайт С.Терехова

2.              http://www.orc.ru/~stasson/neurox.html - много ссылок

3.              http://ai-online.narod.ru/documents-neural_networks.html - (Уоссермен, статьи С.Короткого, Миркес).

4.              http://www.statsoft.ru/home/textbook/modules/stneunet.html - основы

5.              http://chip.ua/links/neuro/ - ссылки на ресурсы по нейросетям

6.              http://algolist.manual.ru/ai/neuro/index.php - выложены книги и статьи

7.              http://www.scintific.narod.ru/neural.htm - множество ссылок, книг и статей

8.              http://neuroschool.narod.ru/ - выложены наиболее популярные книги, статьи

9.              http://alife.narod.ru/lectures/wavelets2001/ - Вейвлет и нейронные сети//С.А.Терехов

10.           http://ieee-nns.org/ -IEEE Neural Networks Society Home Page

11.           http://www.cs.stir.ac.uk/~lss/NNIntro/InvSlides.html -An Introduction to Neural Networks

Избранноедля первого чтения

1.              Ф.Уоссермен. Нейрокомпьютерная техника. Москва «Мир» 1992.

2.              М.Б.Беркинблит, С.Г.Глаголева. Электричество в живых организмах. М.Наука 1988 (библ.”Квант” вып.6)

3.              В.Н.Вапник, А.Я.Червоненскас. Теория распознавания образов. Наука, 1974.

4.              J. Hertz, A.Krogh, R.Palmer. Itroduction to the Theory of Neural Computation. Massachusetts: Addison-Wesley, 1991.

5.              P. Peretto. An introduction to the Modeling of Neural Networks. Cambridge,  University Press, 1992.

6.              B. Muller, J.Reinhardt, M.T.Strickland. Neural Networks. An Introduction. 2nd edition, Springer, 1995.

7.              S. Haykin. Neural Networks. A Comprehensive Foundation. Macmillan, 1994.

8.              M. Arbib, ed. The Handbook of Brain Theory and Neural Networks. MIT Press, 1995.

9.              C.M.Bishop. Neural networks and pattern recognition. Oxford Press, 1995.

I. Books on Pattern Recognition and (Statistical) Learning

1.              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.

2.              M. I. Schlesinger, V. Hlavбc, Ten Lectures on Statistical and Structural  Pattern Recognition, Kluwer Academic Publishers, 2002.

3.              D. J. Hand, H. Mannila and P. Smyth, Principles of Data Mining, MIT Press,  August 2001.

4.              A.Hyvдrinen, J. Karhunen, and E. Oja, Independent Component Analysis, John  Wiley & Sons, 2001.

5.              T. Hastie, R. Tibshirani, and J. Fridman, The Elements of Statistical  Learning: Data Mining, Inference, and Prediction, Springer-Verlag, 2001.

6.              R. O. Duda, P. E. Hart and D. G. Stork, Pattern Classification (2nd ed.), John  Wiley and Sons, 2001.

7.              S. Raudys, Statistical and Neural Classifiers, Springer, 2001.  G.J. McLachlan and D. Peel, Finite Mixture Models, New York: Wiley, 2000.

8.              M. Friedman and A. Kandel, Introduction to Pattern Recognition, statistical,  structural, neural and fuzzy logic approaches, World Scientific, Signapore,  1999.

9.              D. J. Hand, J. N. Kok and M. R. Berthold, Advances in Intelligent Data  Analysis, Springer Verlag, Berlin, 1999.

10.           B. Schulkopf, C. J. C. Burges, and A. J. Smola, Advances in Kernel Methods,  Support Vector Learning MIT Press, Cambridge, 1999.

11.           S. Theodoridis, K. Koutroumbas, Pattern recognition, Academic Press, 1999.  A. Webb, Statistical pattern recognition, Oxford University Press Inc., New  York, 1999.

12.           M. Berthold, D. J. Hand, Intelligent Data Analysis, An Introduction,  Springer-Verlag, 1999.

13.           V. Cherkassky and F. Mulier, Learning from data, concepts, theory and methods,  John Wiley & Sons, New York, 1998.

14.           L. Devroye, L. Gyorfi, G.Lugosi, A Probabilistic Theory of Pattern  Recognition, Springer-Verlag New York, Inc.1996.

15.           E. Gose, R. Johnsonbaugh, S. Jost, Pattern recognition and image analysis,  Pretice Hall Inc., 1996.

16.           J. Schurmann, Pattern classification, a unified view of statistical and neural  approaches, John Wiley & Sons, New York, 1996.

17.           V.N. Vapnik, The Nature of Statistical Learning Theory, Springer,1996.  B. Ripley, Pattern Recognition and Neural Networks, Cambridge University

18.           Press, Cambridge, 1996.  C.M. Bishop, Neural Networks for Pattern Recognition, Clarendon Press, Oxford,  1995.

19.           D. Paulus and J. Hornegger, Pattern Recognition and Image Processing in C++,  Vieweg, Braunschweig, 1995.

20.           R. Schalkhoff, Pattern Recognition, statistical, structural and neural  approaches, John Wiley and Sons, New York, 1992.

21.           G.J. McLachlan, Discriminant Analysis and Statistical Pattern Recognition,  John Wiley and Sons, New York, 1992.

22.           B. V. Dasarathy, Nearest neighbor(nn) norms: NN pattern classification  techniques, IEEE Computer Society Press, Los Alamitos, 1991.

23.           S.M. Weiss and C.A. Kulikowski, Computer Systems that Learn, Morgan Kaufmann,  San Mateo, California, 1991.

24.           K. Fukunaga, Introduction to Statistical Pattern Recognition (Second Edition),  Academic Press, New York, 1990.

25.           Y.H. Pao, Adaptive Pattern Recognition and Neural Networks, Addison Wesley,   Reading, Massachusetts, 1989.

26.           Satoshi Watanabe, Pattern Recognition, Human and Mechanical, John Wiley &  Sons, New York, 1985.

27.           T.Y. Young and K.S. Fu, Handbook of Pattern Recognition and Image Processing,  Academic Press, Orlando, Florida, 1986.

28.           L. Breiman, J.H. Friedman, R.A. Olshen, and C.J. Stone, Classification and  regression trees, Wadsworth, California, 1984.

29.           P.A. Devijver and J. Kittler, Pattern Recognition, a Statistical Approach,  Prentice Hall, Englewood Cliffs, London, 1982.

30.           R.C. Gonzalez and M.G. Thomason, Syntactic pattern recognition - An  introduction, Addison-Wesley, Reading, 1982.

31.           J. Sklanski and G.N. Wassel, Pattern Classifiers and Trainable Machines,  Springer, New York, 1981.

32.           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)

II. Books on Neural Networks

1.              P. Dayan, L.F. Abbott, Theoretical Neuroscience, Computational and  Mathematical Modeling of Neural Systems , MIT Press, December 2001.

2.              U. Seiffert, L.C. Jain (editors), Self-Organizing Neural Networks: Recent  Advances and Applications (Studies in Fuzziness and Soft Computing),  Springer-Verlag, November 2001.

3.              W. Maass and C. M. Bishop, editors, Pulsed Neural Networks, MIT Press,   Cambridge, 1999.

4.              S. Amari, N. Kasabov, Brain-like computing and intelligent information  systems, Springer Verlag, Berlin, 1998.

5.              G. B. Orr, K-R. Mьller (editors), Neural Networks: Tricks of the Trade,  Springer-Verlag Berlin Heildeberg, 1998.

6.              T. Kohonen, Self-Organizing Maps, Springer, Berlin, 1995, 1997.

7.              C. M. Bishop, editor, Neural Networks and Machine Learning 1997 NATO Advanced  Study Institute, Springer 1998.

8.              P. Smolensky, M. C. Mozer, and D. E. Rumelhart, Mathematical Perspectives on  Neural Networks, Lawrence Erlbaum Associates, Inc. Mahwah, New Yersey, 1996.

9.              Y. Bengio, Neural networks for speech and sequence recognition, International  Thomson Publishing, London, 1995.

10.           LiMin Fu, Neural Networks in Computer Intelligence, McGraw-Hill, Inc., New  York, NY, 1994.

11.           S. Haykin, Neural Networks, A Comprehensive Foundation, Macmillan, New York,  NY, 1994.

12.           S.Y. Kung, Digital Neural Networks, Prentice Hall, Englewood Cliffs, NJ, 1993.

13.           Stephen I. Gallant, Neural Network Learning and Expert systems, Massachusetts  Inst. of Technology, Cambridge, Massachusetts, 1993.

14.           Cichocki and R. Unbehauen, Neural Networks for Optimization and Signal  Processing, John Wiley & Sons, New York, 1993.

15.           H. Chen, L. F. Pau, P. S. P. Wang, Handbook of Pattern Recognition and  Computer Vision, World Scientific, Singapore, 1993.

16.           Kosko, Neural networks for signal processing, Prentice-Hall, Englewood  Cliffs, 1992.

17.           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.

18.           John Hertz, Anders Krogh, and Richard G. Palmer, Introduction to the Theory of  Neural Computation, Addison Wesley Publ. Comp., Redwood City ,CA, 1991.

19.           J. Diederich, Artificial neural networks - Concept learning, IEEE Computer  Society Press, Los Alamitos, 1990.

20.           P.D. Wasserman, Neural Computing, theory and practice, Van Nostrand Reinhold,   New York, 1989.

21.         Aleksander, Neural Computing Architectures, North Oxford Academic, London,  1989.

22.           S. Grossberg, The Adaptive Brain I: Cognition, Learning, Reinforcement, and  Rythm, Elsevier/North Holland, Amsterdam, 1987.

23.           S. Grossberg, The Adaptive Brain II: Vision, Speech, Language and Motor  Control, Elsevier/North Holland, Amsterdam, 1987.

III. Books on Machine Learning

1.              D.J.C. MacKay, Information Theory, Inference, and Learning Algorithms,   Cambridge University Press, 2003.

2.              B. Apolloni, D. Malchiodi and S. Gaito, Algorithmic Inference in Machine  Learning, International Series on Advanced Intelligence, Vol. 5, Advanced

3.              Knowledge International, 2003.

4.              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.

5.              B. Schцlkopf and A.J. Smola, Learning with Kernels, Support Vector Machines,  Regularization, Optimization, and Beyond, MIT Press, Cambridge, 2001.

6.              N. Cristinanini and J. Shawe-Taylor, An Introduction to Support Vector  Machines, Cambridge University Press, Cambridge, UK, 2000.

7.              B. Schцlkopf, C.J.C. Burges and A.J. Smola (editors), Advances in Kernel  Methods, Support Vector Learning, MIT Press, Cambridge, 1999.

8.              T.M. Mitchell, Machine learning, Mc Graw-Hill, New York, 1997.

9.              J.R. Quinlan, C4.5: Programs for machine learning, Morgan Kaufmann Publishers,   San Mateo, California, 1993.

10.           B.K. Natarajan, Machine learning, Morgan Kaufmann Publ, San Mateo, CA, 1991.

IV. Books on Signal Processing

1.              Papoulis and S.U. Pillai, Probability, Random Variables and Stochastic  Processes, McGraw-Hill, 4th edition, 2002.

2.              P. Denbigh, System Analysis and Signal Processing, Addison-Wesley, London,  1998.

3.              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.

4.              V.K. Madisetti and D.B. Williams, editors, The Digital Signal Processing  Handbook, IEEE Press/CRC Press, 1997.

5.              D. Eberly, Ridges in Image and Data Analysis Kluwer Academic Publishers,  Boston/Dordrecht/London, 1996.

6.              J. J. K. Ruanidh, W. J. Fitzgerald, Numerical Bayesian Methods Applied to  Signal Processing, Springer Verlag, Berlin, 1996.

7.              G. R. Wilson, K. W. Baugh, M. D. Ladd, and R. D. Priebe, Higher-order  statistical signal processing, Longman, Australia, 1995.

8.              A.Cichocki, R. Unbehauen, Neural Networks for Optimization and Signal  Processing, John Wiley & Sons, New York, 1993.

9.              D. H. Johnson, D. E. Dudgeon, Array signal processing, Prentice-Hall, 1993.

10.           L. Rabiner, B.-H. Juang, Fundamentals of Speech Recognition Prentice-Hall,   Englewood Cliffs, 1993.

11.           B. Kosko, Neural networks for signal processing, Prentice-Hall, Englewood  Cliffs, 1992.

12.           J. G. Proakis, D. G. Manolakis, Digital signal processing - principles,  algorithms and applications, 2nd ed., MacMillan Publ., New York, 1992.

13.           D.E. Dudgeon and R.M. Mersereau, Multidimensional digital signal processing,  Prentice-Hall, Inc, Englewood Cliffs, 1984.

14.           A.V. Oppenheim, A.S. Willsky, and I.T. Young, Signals and Systems,  Prentice-Hall, 1983.

15.           Papoulis, Signal Analysis, McGraw-Hill, 1977.

16.           R.N. Bracewell,The Fourier Transform and its Applications, McGraw-Hill, third  edition, 2000,1965.

17.           Books of Historical Interest

18.           K. Fukunaga, Introduction to Statistical Pattern Recognition (First Edition),  Academic Press, New York, 1972.

19.           J.M. Mendel and K.S. Fu, Adaptive, learning, and pattern recognition systems:  theory and applications, Academic Press, New York, 1970.

20.           M. Minsky and S. Papert, Perceptrons: An Introduction to Computational  Geometry, MIT Press, Cambridge, Mass, 1969.

21.           A.G. Arkadev and E.M. Braverman, Teaching Computers to Recognize Patterns,  Academic Press, London, 1966.

22.           Nilsson, N.J., Learning Machines, McGraw-Hill, New York, 1965.

23.           G.S. Sebestyen, Decision-Making Processes in Pattern Recognition, Macmillan,   New York, 1962.

24.           Rosenblatt, F., Principles of Neurodynamics: Perceptrons and the theory of  brain mechanisms, Spartan Books, Washington, D.C., 1962. \