Machine Learning - Waseda University - Summer 2011
If you have any specific idea in mind, then come to discuss it with me or send me an email.
Otherwise, here is a list of research papers. What I expect you is to write a detailed report summarizing the paper.
You will have to do some additional background reading to understand what is going on but the machine learning
course is a good basis to start! You will run the algorithm in the Matlab code provided (although you can recode
it on your orwn) and then test it on one or two datasets.
- M.
Tipping, Sparse Bayesian Learning and the Relevance Vector Machine,
Journal Machine Learning Research, 2001. Describes a popular
alternative to LASSO in machine learning [pdf] [matlab]
- J. Ting, M. Mistry, J. Peters, S. Schaal, and J. Nakanishi.
A Bayesian Approach to Nonlinear Parameter
Identification for Rigid Body Dynamics, Robotics: Science and Systems (RSS), 2006.
Describes nonlinear regression models to perform parameter identification for rigid body dynamics [pdf] [matlab code]
- J. Ting, A. D'Souza, S. Vijayakumar, and S. Schaal.
Efficient Learning and Feature Selection in High-Dimensional Regression,
Neural Computation, 22(4): 831-886, 2010.
Describes model for high-dimensional regression and proposes an algorithm for real-time incremental learning [preprint] [matlab code]
- D. Lowne, S. Roberts and R. Garnett (2010). Sequential Non-stationary Dynamic Classification with Sparse
Feedback.
Pattern Recognition. 43(3), 897-905, 2010 (application to Brain
Computer Interface): describes a model to perform sequential
classification with approximate inference based on extended Kalman
filter [paper] [matlab code]
- C. Andrieu, N.
de Freitas and A.
Doucet. Robust Full
Bayesian Learning for Radial
Basis Networks. Neural
Computation,
13(10), 2001: describes a Radial Basis function regression model where
number of basis is selected using a full Bayesian approach based on
MCMC [paper] [ [code]
- S. Rogers and M. Girolami,
A Bayesian Regression Approach to the Inference of Regulatory Networks from Gene Expression Data,
Bioinformatics, vol 21, nos 14, 313 - 3137, 2005: describes a
method to infer gene relations using data from gene knockout
experiments. [paper] [matlab code]
- F. Caron and A. Doucet, Efficient
Bayesian Inference for Generalized Bradley-Terry Models, J. Comp. Graph. Statist., 2011 [paper] [matlab code available on request]: describes original EM and MCMC algorithm to perform inference for ranking models.