Results
Weighting and integrating evidence for stochastic simulation in Bayesian networks
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Heuristic search as evidential reasoning
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PRODIGY: An integrated architecture for planning and learning
Carbonell, J. G. | Knoblock, C. A. | Minton, S.
Technical report CMU-CS-89-189, Computer Science Department, Carnegie-Mellon University
The million pound bridge program
In Levy, D. and Beal, D. (Eds.), Heuristic Programming in Artificial Intelligence. Ellis Horwood.
Engineering approach to building complete, intelligent beings
Proc. SPIE - The International Society for Optical Engineering, 1002, 618-625.
Learnability and the Vapnik-Chervonenkis dimension
Blumer, A. | Ehrenfeucht, A. | Haussler, D. | Warmuth, M.
Valiant’s learnability model is extended to learning classes of concepts defined by regions in Euclidean space E”. The methods in this paper lead to a unified treatment of some of Valiant’s results, along with previous results on distribution-free convergence of certain pattern recognition algorithms. It is shown that the essential condition for distribution-free learnability is finiteness of the Vapnik-Chervonenkis dimension, a simple combinatorial parameter of the class of concepts to be learned. Using this parameter, the complexity and closure properties of learnable classes are analyzed, and the necessary and sufftcient conditions are provided for feasible learnability.JACM, 36 (4), 929-65