Reading List
Bayesian decision theory
- [Tenenbaum06]
- Tenenbaum, J. B.; Griffiths, T. L. & Kemp, C.
- Theory-based Bayesian models of inductive learning and
reasoning
- Trends in Cognitive Sciences,
- 2006, 10, 309-318
- [Dietterich02]
- Dietterich, T. G.
- Machine Learning for Sequential Data: A Review
- Structural, Syntactic, and Statistical Pattern
Recognition: Joint Iapr International Workshops Sspr 2002 and Spr 2002,
Windsor, Ontario, Canada, August 6-9, 2002: Proceedings,
- 2002
- [Friedman99]
- Friedman, N.; Getoor, L.; Koller, D. & Pfeffer, A.
- Learning probabilistic relational models
- Proceedings of the Sixteenth International Joint
Conference on Artificial Intelligence,
- 1999, 1300-1309
- [Goldenberg05]
- Goldenberg, A. & Moore, A.
- Bayes Net Graphs to Understand Coauthorship Networks
- KDD Workshop on Link Discovery: Issues,
Approaches and Applications,
- 2005
Kernel methods
- [Grauman05]
- Grauman, K., and T. Darrell.
- Pyramid match kernel: Discriminative classification with sets of image features.
- MIT Computer Science and Artificial Intelligence Laboratory Technical Report, MIT-CSAIL-TR-2005-017
- 2005.
- [Leslie02]
- Leslie, C., E. Eskin, and W. S. Noble.
- The spectrum kernel: A string kernel for SVM protein classification.
- In Proceedings of the 2002 Pacific Symposium on Biocomputing
- 2002
- [Lodhi02]
- Lodhi, H., C. Saunders, J. Shawe-Taylor, N. Cristianini, and C. Watkins.
- Text classification using string kernels.
- The Journal of Machine Learning Research 2:419-444.
- 2002
Regularization and model complexity
- [Lawrence96]
- Lawrence, Steve, C. Lee Giles, and A.C. Tsoi.
What Size Neural Network Gives Optimal Generalization? Convergence Properties of Backpropagation.
UM Computer Science Department. University of Maryland, UMIACS-TR-96-22
1996
- [Mehta95]
- Mehta, M., J. Rissanen, and R. Agrawal.
- MDL-based decision tree pruning.
- In Proceedings of KDD95
1995
- [Roberts00]
- Roberts, S., and H. Pashler.
- How persuasive is a good fit? A comment on theory testing.
Psychological Review 107, no. 2:358-367
2000
Performance evaluation
- [Domingos99]
- Domingos, P.
- MetaCost: a general method for making classifiers cost-sensitive.
- In Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, p. 155-164
- 1999
- [Hand01]
- Hand, David J., and Robert J. Till.
- A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems.
- Machine Learning 45, no. 2:171-186.
- 2001
- [Japkowicz02]
- Japkowicz, N.
- The class imbalance problem: A systematic study.
- Intelligent Data Analysis, 6(5), p.429-449.
2002
Combining multiple classifiers
- [Dietterich00]
- Dietterich, T. G.
- An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization. Machine Learning 40, no. 2: 139-157.
- 2000
- [Mason00]
- Mason, L., J. Baxter, P. Bartlett, and M. Frean.
- Boosting algorithms as gradient descent.
- In Advances in Neural Information Processing Systems, 12:512-518.
- 2000
- [Oza05]
- Oza, N.
- Online bagging and boosting.
- In 2005 IEEE International Conference on Systems, Man and Cybernetics
- 2005
Clustering and density estimation