Pattern recognition books

Last update: Thu Mar 2 10:57:34 EST 2017


Luc's library has now opened its web doors to all McGill University students. The library is in fact my office (room 300N, McConnell Engineering Building), and the books are a subset of my private collection. Any McGill student may borrow any book at any time!



M. Anthony
N. Biggs

Computational Learning Theory [Google]
Cambridge University Press, Cambridge, 1992.

M. Anthony
P.L. Bartlett

Neural Network Learning: Theoretical Foundations [Google]
Cambridge University Press, Cambridge, 1999.

M. Anthony

Discrete Mathematics of Neural Networks Selected Topics [Google]
SIAM, Philadelphia, 2001.

Jean-Yves Audibert

PAC-Bayesian Statistical Learning Theory [Google]
Paris, France, 2004.

Michael W. Berry
Murray Browne (eds)

Lecture Notes in Data Mining [Google]
Singapore, 2006.

Christopher M. Bishop

Pattern Recognition and Machine Learning [Google]
Springer-Verlag, New York, 2006.

C.M. Bishop

Neural Networks for Pattern Recognition [Google]
Clarendon Press, Oxford, 1995.

C.M. Bishop

Neural Networks for Pattern Recognition [Google]
Clarendon Press, Oxford, 1995.

Ulisses M. Braga-Neto
Edward R. Dougherty

Error Estimation for Pattern Recognition [Google]
IEEE Press and Wiley, Piscataway, NJ, 2015.

L. Breiman
J. H. Friedman
R. A. Olshen
C. J. Stone

Classification and Regression Trees [Google]
Wadsworth International, Belmont, CA., 1984.

G.A. Carpenter
S. Grossberg (eds)

Pattern Recognition by self-Organizing Neural Networks [Google]
MIT Press, Cambrige, 1988.

Nicolo Cesa-Bianchi
Gabor Lugosi

Prediction, Learning, and Games [Google]
Cambridge University Press, New York, 2006.

D. Coomans
I. Broeckaert

Potential Pattern Recognition in Chemical and Medical Decision Making [Google]
John Wiley, New York, 1986.

Eduardo Bayro Corrochano

Handbook of Geometric Computing [Google]
Springer-Verlag, Berlin, 2005.

N. Cristianini
J. Shawe-Taylor

An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods [Google]
Cambridge University Press, Cambridge, UK, 2000.

Nelio Cristianini
John Shawe-Taylor

An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods [Google]
Cambridge University Press, Cambridge, UK, 2000.

B.V. Dasarathy

Nearest Neighbor Pattern Classification Techniques [Google]
IEEE Computer Society Press, Los Alamitos, CA, 1991.

Anirban DasGupta

Probability for Statistics and Machine Learning: Fundamentals and Advanced Topics [Google]
Springer, New York, 2011.

Isabelle De Macq

Hyperrectangular Space Partitioning Trees [Google]
Louvain, Belgium, 2004.

Li Deng
Dong Yu

Deep Learning: Methods and Applications [Google]
Now, Delft, The Netherlands, 2014.

P.A. Devijver
J. Kittler

Pattern Recognition: A Statistical Approach [Google]
Prentice-Hall, London, 1982.

Pierre A. Devijver
Josef Kittler (eds)

Pattern Recognition Theory and Applications [Google]
Springer-Verlag, Berlin, 1987.

L. Devroye
L. Györfi
G. Lugosi

A Probabilistic Theory of Pattern Recognition [Google]
Springer-Verlag, New York, 1996.

A.C. Downton
S. Impedovo (eds)

Progress in Handwriting Recognition [Google]
World Scientific Publishing Co., Singapore, 1997.

M. Dror
P. L'Ecuyer
F. Szidarovszky (eds)

Modeling Uncertainty [Google]
Kluwer Academic Publishers, Dordrecht, The Netherlands, 2002.

R. O. Duda
P. E. Hart

Pattern Classification and Scene Analysis [Google]
John Wiley, New York, 1973.

R. O. Duda
P. E. Hart
D. G. Stork

Pattern Classification (2nd ed.) [Google]
John Wiley, New York, 2001.

R.P.W. Duin

On the accuracy of statistical pattern recognizers [Google]
Dutch Efficiency Bureau, Pijnacker, The Netherlands, 1978.

Brendan J. Frey

Graphical Models for Machine Learning and Digital Communication [Google]
MIT Press, Cambridge, MA, 1998.

K.S. Fu

Syntactic Methods in Pattern Recognition [Google]
Academic Press, New York, 1974.

K.S. Fu

Syntactic Pattern Recognition and Applications [Google]
Academic Press, New York, 1982.

L. Györfi (ed.)

Principles of Nonparametric Learning [Google]
Springer-Verlag, Wien, 2002.

L. Györfi
M. Kohler
A. Krzyzak
H. Walk

A Distribution-Free Theory of Conparametric Regression [Google]
Springer-Verlag, New York, 2002.

T. Hastie
R. Tibshirani
J. Friedman

The Elements of Statistical Learning [Google]
Springer-Verlag, New York, 2001.

Ralf Herbrich

Learning Kernel Classifiers Theory and Algorithms [Google]
MIT Press, Cambridge, MA, 2002.

IEEE

Proceedings of the Third International Joint Conference on Pattern Recognition [Google]
IEEE Computer Society, Piscataway, NJ, 1976.

IEEE

Proceedings of the 1973 IEEE Conference on Decision and Control [Google]
IEEE, New York, 1993.

A.K. Jain
R.C. Dubes

Algorithms for Clustering Data [Google]
Prentice Hall, Englewood Cliffs, NJ, 1988.

F. Jelinek

Statistical Methods for Speech Recognition [Google]
MIT Press, Cambridge, MA, 1998.

Ludmila I. Kuncheva

Combining Pattern Classifiers Methods and Algorithms [Google]
John Wiley, New York, 2004.

S.Y. Kung

Kernel Methods and Machine Learning [Google]
Cambridge University Press, Cambridge, UK, 2014.

G. Lorette

Proceedings of the 1996 French-Korean Workshop on Man-Machine Handwritten Communication [Google]
CNRS, Paris, 1996.

Gabor Lugosi
Hans Ulrich Simon (eds)

Learning Theory 19th Annual Conference on Learning Theory COLT 2006 [Google]
Springer-Verlag, Berlin, 2006.

David J. Marchette

Random Graphs for Statistical Pattern Recognition [Google]
John Wiley, Hoboken, NJ, 2003.

G.J. McLachlan

Discriminant Analysis and Statistical Pattern Recognition [Google]
John Wiley, New York, 1992.

Mehryar Mohri
Afshin Rostamizadeh
Ameet Talwalkar

Foundations of Machine Learning [Google]
MIT Press, Cambridge, MA, 2012.

M. Mozer
M. Jordan
T. Petsche (eds.)

Neural Information Processing Systems [Google]
MIT Press, Cambridge, MA, 1997.

D.W. Murray
B.F. Buxton

Experiments in the Machine Interpretation of Visual Motion [Google]
MIT Press, Cambridge, MA, 1990.

S.K. Pal
P.P. Wang (eds)

Genetic Algorithms for Pattern Recognition [Google]
CRC Press, Boca Raton, FL, 1996.

R. Plamondon
C.Y. Suen
M.L. Simmer (eds)

Computer Recognition and Human Production of Handwriting [Google]
World Scientific, Singapore, 1989.

Sarunas Raudys

Statistical and Neural Classifiers [Google]
Springer-Verlag, London, 2001.

Robert E. Schapire
Yoav Freund

Boosting Fondations and Algorithms [Google]
MIT Press, Cambridge, MA, 2012.

W.F. Schmidt

Neural Pattern Classifying Systems (Ph.D. dissertation) [Google]
Technical University of Delft, Delft, The Netherlands, 1994.

B. Schölkopf
C.J.C. Burges
A.J. Smola (eds)

Advances in Kernel Methods Support Vector Learning [Google]
MIT Press, Cambridge, MA, 1999.

Bernhard Schölkopf
Alexander J. Smola

Learning with Kernels [Google]
MIT Press, Cambridge, MA, 2002.

J. Schürmann

Pattern Classification: A Unified View of Statistical and Neural Approaches [Google]
John Wiley, New York, 1996.

I.K. Sethi
A.K. Jain (eds)

Artificial Neural Networks and Statistical Pattern Recognition [Google]
North Holland, Amsterdam, 1991.

J. Shawe-Taylor
N. Cristianini

Kernel Methods for Pattern Analysis [Google]
Cambridge University Press, Cambridge, UK, 2004.

A.J. Smola
P.L. Bartlett
B. Schölkopf
D. Schuurmans(eds)

Advances in Large Margin Classifiers [Google]
MIT Press, Cambridge, MA, 2000.

Johan Suykens
Gábor Horváth
Sankar Bau
Charles Micchelli
Joos Vandewalle (eds)

Advances in Learning Theory: Methods, Models and Applications [Google]
IOS Press, Amsterdam, 2003.

J.T. Tou
R.C. Gonzalez

Pattern Recognition Principles [Google]
Addison-Wesley, Reading, MA, 1974.

Leslie Valiant

Probably Approximately Correct [Google]
Basic Books, New York, 2013.

Leslie Valiant

Probably Approximately Correct [Google]
Basic Books, New York, 2013.

V. N. Vapnik

Estimation of Dependences Based on Empirical Data [Google]
Springer-Verlag, New York, 1982.

Vladimir Vapnik

Statistical Learning Theory [Google]
John Wiley, New York, 1998.

Vladimir Vapnik

Estimation of Dependences Based on Empirical Data [Google]
Springer Science and Business Media, New York, 2006.

V.N. Vapnik

The Nature of Statistical Learning Theory [Google]
Springer-Verlag, New York, 1995.

V.N. Vapnik

The Nature of Statistical Learning Theory (2nd ed) [Google]
Springer-Verlag, New York, 2000.

D. Wolpert (ed)

The Mathematics of Generalization: Proceedings of the SFI/CNLS Workshop on Formal Approaches to Supervised Learning [Google]
Addison-Wesley, Reading, MA, 1994.



Contact

Luc Devroye
School of Computer Science
McGill University
Montreal, Canada H3A 2K6
lucdevroye@gmail.com
http://cg.scs.carleton.ca/~luc