More to be added soon:

Review slides on probability and linear algebra by Gutierrez-Osuna.

 

Lawrence Rabiner, 1989. A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition.

 

Model Selection and the Principle of Minimum Description Length, Mark H. Hansen and Bin Yu, Journal of the American Statistical Association, Vol 96, 2001.

 

[Heckermann1996] D. Heckermann,  A Tutorial on Learning with Bayesian networks, 1996.

[Cheng 2002b] Cheng, J., Greiner, R., Kelly, J., Bell, DA and Liu, W., Learning Bayesian Networks from Data: an Information-Theory Based Approach, The Artificial Intelligence Journal, Volume 137, Pages 43-90, 2002.

J. Berger and L.R. Pericchi, Objective Bayesian methods for model selection: introduction and comparison

[Bilmes2002]Jeff Bilmes and Geoffrey Zweig, The Graphical Models Toolkit, 2002.

 

What is ICA?

FastICA MATLAB package

A Hyvarinen, E Oja, Independent component analysis: Algorithms and applications, Neural Networks, 2000.

AM Martinez, AC Kak, PCA versus LDA, Pattern Analysis and Machine Intelligence, IEEE Transactions, 2001.

B. Draper, et.al., Recognizing Faces with PCA and ICA, CVIU, (91):1-2,115-137, 2003.

E Bingham, J Kuusisto, K Lagus, ICA and SOM in text document analysis, Proceedings of the 25th ACM SIGIR, 2002.

 

J Söding,  Protein homology detection by HMM–HMM comparison,  Bioinformatics, 2005.

 

A Tutorial on Support Vector Machines for Pattern Recognition by Chris Burgess, 1999.

Advances in Kernel Methods - Support Vector Learning, by Bernhard Schölkopf, Chris Burges, Alex J. Smola, 1999.

 

[Cheng 2002a] Cheng, J., Hatzis, C., Hayashi, H., Krogel, M., Morishita, S., Page, D. and Sese, J., KDD Cup 2001 Report. ACM SIGKDD Explorations Volume3, Issue 2, January 2002.

 

J. Kittler, On Combining Classifiers, PAMI, 1998.

 

T. K. Ho, Complexity Mesures of Supervised Classification Problems, PAMI, 2002.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

References:
  • [EA] Ethem Alpaydýn, “Introduction to Machine Learning (Adaptive Computation and Machine Learning)”, The MIT Press, 2004.
  • [CB] Chris Bishop, Pattern Recognition and Machine Learning, Springer 2006.
  • [DHS] Richard O. Duda, Peter E. Hart, and David G. Stork, Pattern Classification, 2nd Edition, 2000, Wiley.
  • [TM] Tom Mitchell,  Machine Learning, 1997 McGraw Hill.
  • Recent papers on different pattern recognition topics.
  • Matlab Introductory Material

Prepared by Zehra Cataltepe, February  2007.