Reading Assignment 2
Here are four different papers dealing with support vector machines,
and comparing their performance to toher learning techniques. You
have to choose one of these papers, read it and write a short
summary (maximum two pages, 10 point font). Your summary should
contain the following points (not necessarily in this order):
- The main idea expressed in the paper
- The theoretical justification
of the idea
- Empirical support provided by the experiments in the
- What you liked or disliked about the paper
- Questions you had about the paper, aspects you did not understand
You should bring your summary to class on Thursday, October 25.
Be prepared to shortly summarize the paper in front of your colleagues
and participate in discussion.
The papers for this reading assignment are:
Y. LeCun, L. D. Jackel, L. Bottou, C. Cortes, J. S. Denker, H. Drucker, I. Guyon, U. A. Muller, E. Sackinger, P. Simard, and V. Vapnik (1995). "Learning
Algorithms For Classification: A Comparison On Handwritten Digit Recognition". In Neural Networks: The Statistical Mechanics Perspective, (J. H.
Oh, C. Kwon, and S. Cho, eds.), pp. 261-276.
T. Joachims. (1998). "Text categorization with support vector machines: Learning with many relevant features". In Claire NÚdellec and CÚline
Rouveirol, editors, Proceedings of the European Conference on Machine Learning, pages 137-142, Springer.
M. Pontil and A. Verri. (1998).
"Support Vector Machines for 3D Object Recognition".
IEEE Trans. on PAMI, Vol. 20, No. 6, pp 637-646.
C. Ding and I. Dubchak. "Multi-class protein fold recognition using support vector machines and neural networks". Bioinformatics,
Prof. Doina PRECUP
Last modified: Mon Oct 22 17:22:09 EDT 2001