Machine Learning, Volume 29, 1997

Volume 29, Number 1, October 1997

• Paul E. Utgoff, Neil C. Berkman, Jeffery A. Clouse: Decision Tree Induction Based on Efficient Tree Restructuring. 5-44 BibTeX
• Shai Ben-David, Eyal Kushilevitz, Yishay Mansour: Online Learning versus Offline Learning. 45-63 BibTeX
• Kenneth Basye, Thomas Dean, Jeffrey Scott Vitter: Coping with Uncertainty in Map Learning. 65-88 BibTeX

Volume 29, Numbers 2-3, November 1997

• Pat Langley, Gregory M. Provan, Padhraic Smyth: Learning with Probabilistic Representations. 91-101 BibTeX
• Pedro Domingos, Michael J. Pazzani: On the Optimality of the Simple Bayesian Classifier under Zero-One Loss. 103-130 BibTeX
• Nir Friedman, Dan Geiger, Moisés Goldszmidt: Bayesian Network Classifiers. 131-163 BibTeX
• Sanjoy Dasgupta: The Sample Complexity of Learning Fixed-Structure Bayesian Networks. 165-180 BibTeX
• David Maxwell Chickering, David Heckerman: Efficient Approximations for the Marginal Likelihood of Bayesian Networks with Hidden Variables. 181-212 BibTeX
• John Binder, Daphne Koller, Stuart J. Russell, Keiji Kanazawa: Adaptive Probabilistic Networks with Hidden Variables. 213-244 BibTeX
• Zoubin Ghahramani, Michael I. Jordan: Factorial Hidden Markov Models. 245-273 BibTeX
• Naoki Abe, Hiroshi Mamitsuka: Predicting Protein Secondary Structure Using Stochastic Tree Grammars. 275-301 BibTeX