Seminar in Computational Learning: Lectures

Num. Topic Date Source
exercises
1 Introduction on Computational Learning 30.10.00 [KV] Chapter 1
2 Definition of PAC model 6.11.00 [KV] Chapter 1
3 Occam's Razor Algorithms. 13.11.00 [KV] Chapter 2
4 Weak and Strong Learning: Adaboost. 20.11.00 [FS]
5 Boosting System for Text Categorization. 27.11.00 [SS]
6-7 Learning with Classification Noise. 4.12.00, 11.12.00 [KV] Chapter 5, [Kearns]
8 Noise-tolerant Learning and the Parity problem. 18.12.00 [BKW]
9 Learning in the Presence of Malicious Noise. 25.12.00 [KL]
10 Learning in the On-line Model. 1.1.01  
11 The Winnow Algorithm. 8.1.01 [Littlestone]
12 Subexponential learning of DNF. 15.1.01 [Bshouty]
13 Seminar Summary. 22.1.01  

Bibliography

[BKW]
A. Blum, A. Kalai and H. Wasserman. Noise-tolerant Learning, the Parity problem, and the Statistical Query model. STOC'00, pages 435--440, 2000.
[Bshouty]
N. H. Bshouty. A subexponential exact learning algorithm for DNF using equivalence queries. Information Processing Letters, 59(1):37--39, 1996.
[FS]
Y. Freund and R. E. 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.
[Kearns]
M. Kearns. Efficient noise-tolerant learning from statistical queries. J. of the ACM, 45(6):983--1006, 1998.
[KL]
M. Kearns and M. Li. Learning in the Presence of Malicious Errors. SIAM Journal on Computing. 22(4):807-837, 1993.
[KV]
M. Kearns and U. V. Vazirani. An Introduction to Computational Learning Theory. MIT Press, 1994.
[Littlestone]
N. Littlestone, Learning When Irrelevant Attributes Abound: A New Linear-Threshold Algorithm, Machine Learning, 2:285--318, 1988.
[SS]
R. E. Schapire and Y. Singer. BoosTexter: A boosting-based system for text categorization. Machine Learning, 39(2/3):135-168, 2000.