MLnet Summer School on Machine Learning and Knowledge Acquisition: LEARNING AND PROBABILITIES

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by Wray Buntine

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249 Operations for Learning with Graphical Models – Wray L. Buntine - 1994
7 Prior Information and Generalized Questions – Jörg C. Lemm - 1996
5 Probabilistic Inference from Arbitrary Uncertainty using Mixtures of Factorized Generalized Gaussians – Alberto Ruiz, M. Carmen Garrido - 1998
564 Dynamic Bayesian Networks: Representation, Inference and Learning – Kevin Patrick Murphy - 2002
1 Divide and Conquer: Pattern Recognition using Mixtures of Experts – Steve Waterhouse, Steve Waterhouse - 1997
723 Hierarchical mixtures of experts and the EM algorithm – Michael I. Jordan - 1994
3 Nonparametric regression for learning – Stefan Schaal - 1994
8 Nonparametric Regression for Learning Nonlinear Transformations – Stefan Schaal
35 Classification and Regression using Mixtures of Experts – Steven Richard Waterhouse - 1997
36 Learning Probabilistic Networks – Paul J Krause - 1998
2 Stochastic Complexity Based Estimation of Missing Elements in Questionnaire Data – Henry Tirri, Tomi Silander - 1998
172 A Guide to the Literature on Learning Probabilistic Networks From Data – Wray Buntine - 1996
19 An Anytime Approach To Connectionist Theory Refinement: Refining The Topologies Of Knowledge-Based Neural Networks – David William Opitz - 1995
Bayesian Modelling in Machine Learning: A Tutorial Review – Matthias Seeger - 2009
2 A new approach to fitting linear models in high dimensional spaces – Yong Wang - 2000
8 Probabilistic Curve-Aligned Clustering and Prediction with Regression Mixture Models – Scott John Gaffney - 2004
5 Performance and efficiency: Recent advances in supervised learning – Sheng Ma, Chuanyi Ji - 1999
14 Theory refinement of bayesian networks with hidden variables – Sowmya Ramachandran - 1998
5 Mathematical Programming Approaches To Machine Learning And Data Mining – Paul S. Bradley - 1998