Reading List

Machine Learning
2009-I


Bayesian decision theory

[Goldenberg05]
Goldenberg, A. & Moore, A.
Bayes Net Graphs to Understand Coauthorship Networks
KDD Workshop on Link Discovery: Issues, Approaches and Applications, 2005
[MacKay98] 
D.J.C. MacKay
Introduction to monte carlo methods
Learning in graphical models, Kluwer, 1998, pp. 175–204.
[Myers99] 
J.W. Myers, K.B. Laskey, and T.S. Levitt
Learning Bayesian networks from incomplete data with stochastic search algorithms
Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann Publishers, 1999, pp. 476-485.
[Rabiner89]
L.R. Rabiner 
A tutorial on hidden Markov models and selected applications in speech recognition
Proceedings of the IEEE,  vol. 77, 1989, pp. 257-286.
[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
[Yesidia03] 
J.S. Yedidia, W.T. Freeman, and Y. Weiss
Understanding belief propagation and its generalizations
Exploring artificial intelligence in the new millennium, Morgan Kaufmann, 2003, pp. 239–236.

Kernel methods

[Borgwardt05]
 K.M. Borgwardt, C.S. Ong, S. Schonauer, S.V.N. Vishwanathan, A.J. Smola, and H.P. Kriegel, 
Protein function prediction via graph kernels
Bioinformatics,  vol. 21, 2005, pp. i47-i56.

Support Vector Learning

[Hsu02] 
C.W. Hsu and C.J. Lin
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks,  vol. 13, 2002, pp. 415-425.
[Smola04] 
A.J. Smola and B. Schölkopf
A tutorial on support vector regression
Statistics and Computing,  vol. 14, 2004, pp. 199-222.
[Tong01] 
S. Tong and E. Chang
Support vector machine active learning for image retrieval
Proceedings of the ninth ACM international conference on Multimedia, ACM New York, NY, USA, 2001, pp. 107-118.

Performance evaluation


Learning on complex-structured and non-structured data