- Maximum entropy course notes
- Adam Berger's MaxEnt resources Gentle tutorials, pointers to papers on Maxent in Language.
- S. Della Pietra, V. Della Pietra, and J. Lafferty. Inducing features of random fields. IEEE Transactions on pattern analysis and machine intelligence, 19(4), 380-393, April, 1997
- T. Jaakkola, M. Meila, T. Jebara "Maximum Entropy Discrimination"
- Zhang Le's Maxent page Another list of tutorials and papers (Note that the links to papers are allrotten since they are through citeseer).
- Maximum Entropy On-line Resources (mostly past MaxEnt conferences)
- Skilling, J. 1989. Classic maximum entropy. In: Maximum Entropy and Bayesian Methods. J. Skilling, editor. Kluwer Academic, Norwell, MA. 45-52.
- The relation of Bayesian and Maximum Entropy methods", E.T. Jaynes, 1988
- J. Darroch and D. Ratcliff. Generalized iterative scaling for log-linear models. Ann. Math. Statistics, 43:1470-1480, 1972.
- A. Berger, S. Della Pietra, and V. Della Pietra. A maximum entropy approach to natural language processing. Computational Linguistics, 22(1):39-71, 1996.
- S. Guiasu and A. Shenitzer. The principle of maximum entropy. The Mathematical Intelligencer, 7(1), 1985. (An overview paper)
- Y. Freund, "Boosting a weak learning algorithm by majority",
Information and Computation, 9:1545-1588, 1997.
- Y. Freund, and R. Schapire, "Experiments with a new boosting
algorithm", in Machine Learning: Proceedings of the Thirteenth International
Conference, pp. 148-156, 1996.
- R. Schapire, Y. Freund, P. Bartlett, and W. S. Lee, "Boosting the margin:
a new explanation for the effectiveness of voting methods", in Machine Learning:
Proceedings of the Fourteenth Interbational Conference, 1997.
- J. Friedman, T. hastie, and R. Tibshirani,
"Additive logistic regression: a statistical view of boosting", Annals
of Statistics, 2000.
- H. Drucker, and C. Cortes, "Boosting decision trees",
in Advances in Neural Information Processing Systems 8, pp. 479-485, 1996.
On Error Bounds, and combining classifiers
V. Koltchinskii, D. Panchenko, "Some new bounds on the generalization error of
combined classifiers", NIPS 2000, pp. 245-251.
- A. Murua, "Upper bounds for error rates associated to linear combination
of classifiers", IEEE PAMI, May 2002.
- E. Bauer, and R. Kohavi, "An empirical comparison of voting classification
algorithms: bagging, boosting, and variants", Machine Learning, 36, 105-142, 1999.
On Bagging, Arcing, and Random Forests
- Y. Amit, and A. Murua, "Speech recognition using randomized relational decision trees",
IEEE, Trans. Speech and Audio Processing, 9, May 2001.
- Y. Amit, and D. Geman, "Shape quantization and recognition with randomized trees",
Neural Computation 9, pp. 1545-1588, 1997.
- L. Breiman, "Bagging predictors", Machine Learning, 24(2):123-140, 1996.
- L. Breiman, "RF/TOOLS: A Class of Two-eyed Algorithms",
SIAM Workshop, May 2003, Statistics Department, UCB.
- L. Breiman, "Random Forests", 2001, Statistics Department, UCB.
- L. Breiman, "Prediction games and arcing algorithms",
Statistics Department, UCB.
- Dimension reduction
- EM Algorithms for PCA and SPCA.Sam Roweis.
- Experiments with random projection, Sanjoy Dasgupta,
Uncertainty in Artificial Intelligence (UAI), 2000.
- Bayesian Multidimensional Scaling and Choice of Dimension Oh, M.-S. and Raftery, A.E., Journal of the American Statistical Association, 96, 1031-1044.(2001)
- Bayesian PCA. Bishop, C. M. In M. S. Kearns, S. A. Solla, and D. A. Cohn (Eds.), Advances in Neural Information Processing Systems, Volume 11, pp. 382--388. MIT Press.
- Locally Linear Embedding (LLE) homepage Read "An introduction to Locally Linear Embedding" in the Publications page
- Hessian Eigenmaps: New Locally-Linear Embedding Techniques for High-Dimensional Data Carrie Grimes, David Donoho