# 6.891 Machine Learning and Neural Networks

## Related Info

**EM**
**Ockham's Razor **
**Bayes Nets**
**Face Detection**
Sung and Poggio:
The paper: Example Based Learning for View-Based Human Face Detection
The web site: Face Detection
Rowley, Baluja, and Kanade:
Two papers:
Neural Network-Based Face Detection
Rotation Invariant Neural Network-Based Face Detection
The web site: Face
Detection Project
The demo: CMU's
Face Detector Demo
**Support Vector Machines **
Manual for SVM C programs here.
This is a good starting
point.
Here is a reasonable tutorial.
The Lucent SVM web page.
Royal Holloway, University of London, Department of Computer Science
also does work on SVM's.
Check out there nice web demo....
Some matlab code for SVM's here.
A ``Simple Learning Algorithms for Training Support
Vector Machines'' by Campbell and Cristianini is a paper which describes a simple approach
to finding perceptrons with large margin.
Campbell's web page is
here.
**Numerical Recipes in C **
This truly fantastic book is actually available
on-line.
The chapter that describes linear programming is available
here.
**Independent Component Analysis**
The paper on separating images by Hany Farid is
here
Tony Bell has a good
web page
listing many of the important papers.
From that web site, the following are good starting points:
Bell A.J. and Sejnowski T.J. 1995. An information maximisation approach
to blind separation and blind deconvolution, *Neural Computation,*
7, 6, 1129-1159 Abstract
, Paper (0.9MB),
Compressed (0.3MB)
(38 pages). [3 short conference papers on the same material: ICASSP
95 (1.4MB, 4 pages) , NIPS
94 (0.2MB, 8 pages), and NOLTA95. ]
Pearlmutter B.A. and Parra L.C. 1996. A context-sensitive generalization
of ICA, *Proc. ICONIP '96, *Japan Paper
John
Fisher also has a
page
with his ICA papers including
J. W. Fisher III, and J. C. Principe, Unsupervised learning for nonlinear
synthetic discriminant functions, *Proceeding of SPIE*,
vol. 2752, Orlando, April, 1996.
**Boosting, Bagging, Etc.**
The following reference is the best for beginning to read about
boosting. Freund's homepage has an example applet.
Yoav Freund and
Robert Schapire.
A decision-theoretic generalization of
on-line learning and an application to boosting.
*Journal of Computer and System Sciences*, 55(1):119-139, 1997.
Schapire also has a nice listing of more rescent papers and work on
boosting.
If you like the word bagging more: Leo Breiman. Bagging Predictors. Tech-Report #421, September 1994. Department of Statistics, UCB.
**Tangent Spaces and Mixtures of PCA**
Trevor Hastie has a page listing a number of his papers (mostly from 1994) on these kinds of methods including:
Hastie, T. J., Simard, P. Y., and Saeckinger, E.
"Learning Prototype Models for
Tangent Distance." NIPS proceedings, 1994.
Other similar papers can be found at Chris Atkeson's page about local learning.
*Efficient pattern recognition using a new
transformation distance* by P. Simard, Y. LeCun and J. Denker. In
"Advances in Neural Information Processing Systems V", Morgan Kaufmann
Publishers, pp. 50--58, 1993. is also a nice article. I have a copy in
my office if you'd like to photocopy it.
Tipping M. E. and Bishop C. M. Mixtures of Probabilistic Principal
Component Analysers Tech-report NCRG/97/003, June 11, 1997. is also
a nice paper on a similar technique.

Paul A. Viola
Last modified: Sun Nov 28 13:51:36 EST 1999