Reading List

Machine Learning

Bayesian decision theory

Goldenberg, A. & Moore, A.
Bayes Net Graphs to Understand Coauthorship Networks
KDD Workshop on Link Discovery: Issues, Approaches and Applications, 2005
D.J.C. MacKay
Introduction to monte carlo methods
Learning in graphical models, Kluwer, 1998, pp. 175–204.
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.
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.
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
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

 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

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.
A.J. Smola and B. Schölkopf
A tutorial on support vector regression
Statistics and Computing,  vol. 14, 2004, pp. 199-222.
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