Project
From Course 6.867
Contents |
Data repositories
- The UC Irvine repository
- Character recognition
- The PASCAL Visual Object Classification Challenge
- Enron: A Dataset for Email Classification
- Want to know how SIRI [1] recognizes your speech? Let's try English Phonetic Classification
- Can we use News to predict stock performance? Wall Street Journal vs. Dow Jones
Machine learning algorithm libraries
- Shogun large-scale machine learning matlab toolbox
- Matlab statistics toolbox
- scikit.learn: Python machine learning modules
- Papers and implementations of open-source machine-learning software
Journals and conferences
Journal papers are usually easier to read, because they are longer and have better exposition and motivation.
- Journal of Machine Learning
- Machine Learning Journal URL provided is for MIT access only
- NIPS Conferences
- International Conference on Machine Learning URL for 2011 proceedings
Possible papers to read and replicate
Supervised Classification
- Fei Sha and Lawrence K. Saul, Large Margin Hidden Markov Models.
- Advances of Neural Information and Processing System, 2006. paper
- Ryan Rifkin and Aldebaro Klautau, In Defense of One-vs-All Classification.
- Journal of Machine Learning Research, Volume 5 (Jan): 101-141, 2004. paper
- Ben Taskar, and Carlos Guestrin and Daphne Koller, Max-Margin Markov Networks.
- Advances of Neural Information and Processing System, 2003. paper
- Andrew Y. Ng and Michael I. Jordan, On Discriminative vs. Generative Classifiers: A Comparison of Logistic Regression and Naive Bayes.
- Advances of Neural Information and Processing System, 2001. paper
- Yoav Freund and Robert E. Schapire, Large Margin Classification Using the Perceptron Algorithm.
- Machine Learning, Volume 37, Issue 3, 1999. paper
- Robert E. Schapire, Yoav Freund, Peter Bartlett, and Wee Sun Lee, Boosting the Margin: A New Explanation for the Effectiveness of Voting Methods.
- The Annals of Statistics, Volume 26, Issue 5, 1998. paper
- Thorsten Joachims, Text Categorization with Suport Vector Machines: Learning with Many Relevant Features.
- Proceedings of the European Conference on Machine Learning, 1998. paper
- Rich Caruana, Multitask Learning.
- Machine Learning, Volume 28, 41-75, 1997. paper
- Nick Littlestone, Learning Quickly when Irrelevant Attributes Abound: A New Linear-Threshold Algorithm.
- Machine Learning, Volume 2, Issue 4, 1988.
- And see later work on multiplicative update algorithms.
Semi-supervised Classification
- Andreas Argyriou, Mark Herbster, and Massimiliano Pontil, Combining Graph Laplacians for Semiâ€“Supervised Learning.
- Advances of Neural Information and Processing Systems, 2005. paper
- Thorsten Joachims, Transductive Inference for Text Classification Using Support Vector Machines.
- Proceedings of International Conference on Machine Learning, 1999. paper
- Avrim Blum and Tom Mitchell, Combining Labeled and Unlabeled data with Co-Training.
- Proceedings of the 11th Annual Conference on Computational Learning Theory, 1998. paper
Unsupervised Learning
- Lawrence K. Saul and Sam T. Roweis, Think Globally, Fit Locally: Unsupervised Learning of Low-Dimensional Manifolds,
- Journal of Machine Learning Research, Volume 4, 119-155, 2003. paper
- Michael E. Tipping and Christopher M. Bishop, Probabilistic Principal Component Analysis,
- Journal of Royal Statistics Society (B), Volume 61, Part 3, 611-622, 1999. paper
Reinforcement Learning
- T. G. Dietterich, Hierarchical Reinforcement Leanring with the MAXQ Value Function Decomposition.
- Journal of Artificial Intelligence Research, Volume 13, 227-303, 2000. paper
Graphical Models and Inference
- Chong Wang, Bo Thiesson, Christopher Meek, and David Blei, Markov Topic Models.
- Proceedings of the 12th International Conference on Artificial Intelligence and Statistics (AISTATS) 2009. paper
- David Blei and Jon McAuliffe, Supervised Topic Models.
- Advances of Neural Information and Processing Systems, 2007. paper
- David M. Blei, Andrew Y. Ng, and Michael I. Jordan, Latent Dirichlet Allocation.
- Journal of Machine Learning Research, Volume 3, 993-1022, 2003. paper
- Zhe Chen, Bayesian Filtering: From Kalman Filters to Particle Filters, and Beyond.
- Statistics, 2003. paper
- Jonathan S. Yedidia and William T. Freeman, and Yair Weiss, Understanding Belief Propagation and Its Generalization.
- Exploring Artificial Intelligence in the New Millennium, Chap. 8, pp. 239-236, 2003. paper
- Matthew J. Beal and Zoubin Ghahramani, The Variational Bayesian EM Algorithm for Incomplete Data: with Application to Scoring Graphical Model Structures.
- Bayesian Statistics 7, 2003. paper
- John Lafferty, Andrew McCallum, and Fernando Pereira, Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data.
- Proceedings of 18th International Conference on Machine Learning, 2001. paper
- C.K.I. Williams, Prediction with Gaussian Processes: From Linear Regression to Linear Prediction.
- Learning in Graphical Models, pages 599-621. The MIT Press, 1999. paper
Model Selection
- Michael Kearns, Yishay Mansour, Andrew Y. Ng, and Dana Ron, An Experimental and Theoretical Comparison of Model Selection Methods.
- Proceeding of 18th Annual Conference on Computational Learning Theory, 1995. paper
Others
- Guy Shani, Davic Heckerman, and Ronen I. Brafman, An MDP-Based Recommender System.
- Journal of Machine Learning Research, Volume 6, 1265-1295, 2005. paper
- Thomas Gaertner, John Lloyd, and Peter Flach, Kernels and Distances for Structured Data.
- Machine Learning, Volume 57 Issue 3, 2004. paper
- You'd need to know something about logic to pursue this one.