1. Learning with Probabilistic Representations

   • Pat Langley
   • Gregory M. Provan
   • Padhraic Smyth

   OriginalPaper Pages: 91 - 101

2. On the Optimality of the Simple Bayesian Classifier under Zero-One Loss

   • Pedro Domingos
   • Michael Pazzani

   OriginalPaper Pages: 103 - 130

3. Bayesian Network Classifiers

   • Nir Friedman
   • Dan Geiger
   • Moises Goldszmidt

   OriginalPaper Pages: 131 - 163

4. The Sample Complexity of Learning Fixed-Structure Bayesian Networks

   • Sanjoy Dasgupta

   OriginalPaper Pages: 165 - 180

5. Efficient Approximations for the Marginal Likelihood of Bayesian Networks with Hidden Variables

   • David Maxwell Chickering
   • David Heckerman

   OriginalPaper Pages: 181 - 212

6. Adaptive Probabilistic Networks with Hidden Variables

   • John Binder
   • Daphne Koller
   • Keiji Kanazawa

   OriginalPaper Pages: 213 - 244

7. Factorial Hidden Markov Models

   • Zoubin Ghahramani
   • Michael I. Jordan

   OriginalPaper Pages: 245 - 273

8. Predicting Protein Secondary Structure Using Stochastic Tree Grammars

   • Naoki Abe
   • Hiroshi Mamitsuka

   OriginalPaper Pages: 275 - 301