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
- [Domingos99]
- Domingos, P.
- MetaCost: a general method for making classifiers cost-sensitive.
- In Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, p. 155-164
- 1999
- [Hand01]
- Hand, David J., and Robert J. Till.
- A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems.
- Machine Learning 45, no. 2:171-186.
- 2001
- [Japkowicz02]
- Japkowicz, N.
- The class imbalance problem: A systematic study.
- Intelligent Data Analysis, 6(5), p.429-449.
2002
- [Demsar06]
- J. Demšar
- Statistical Comparisons of Classifiers over Multiple Data Sets
- J. Mach. Learn. Res., vol. 7, pp. 1-30
- 2006
Combining multiple classifiers
- [Dietterich00]
- Dietterich, T. G.
- An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization. Machine Learning 40, no. 2: 139-157.
- 2000
- [Mason00]
- Mason, L., J. Baxter, P. Bartlett, and M. Frean.
- Boosting algorithms as gradient descent.
- In Advances in Neural Information Processing Systems, 12:512-518.
- 2000
- [Oza05]
- Oza, N.
- Online bagging and boosting.
- In 2005 IEEE International Conference on Systems, Man and Cybernetics
- 2005
Learning on complex-structured and non-structured data