We present tractable, exact algorithms for learning actions' effects and preconditions in partially observable domains. Our algorithms maintain a propositional logical repres...
We propose a novel method of dimensionality reduction for supervised learning. Given a regression or classification problem in which we wish to predict a variable Y from an expla...
Kenji Fukumizu, Francis R. Bach, Michael I. Jordan
The design of effective neighborhood structures is fundamentally important for creating better local search and metaheuristic algorithms for combinatorial optimization. Significant...
An uncertainty model for an expensive function greatly improves the effectiveness of a design decision based on the use of a less accurate function. In this paper, we propose a met...
J. Umakant, K. Sudhakar, P. M. Mujumdar, C. Raghav...
This paper presents a new approach to ranking relevant images for retrieval. Distance in the feature space associated with a kernel is used to rank relevant images. An adaptive qu...