# Reading List

### 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