• Publications
  • Influence
Artificial Intelligence : A Modern Approach
The long-anticipated revision of this #1 selling book offers the most comprehensive, state of the art introduction to the theory and practice of artificial intelligence for modern applications.Expand
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Distance Metric Learning with Application to Clustering with Side-Information
Many algorithms rely critically on being given a good metric over their inputs. For instance, data can often be clustered in many "plausible" ways, and if a clustering algorithm such as K-meansExpand
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Algorithms for Inverse Reinforcement Learning
Objective—To evaluate the pharmacokinetics of a novel commercial formulation of ivermectin after administration to goats. Animals—6 healthy adult goats. Procedure—Ivermectin (200 μg/kg) was initiallyExpand
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Artificial intelligence - a modern approach, 2nd Edition
mathematical description; the agent program is a concrete implementation, running within some physical system. To illustrate these ideas, we use a very simple example—the vacuum-cleaner world shownExpand
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Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping
This paper investigates conditions under which modi cations to the reward function of a Markov decision process preserve the op timal policy It is shown that besides the positive linearExpand
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Online bagging and boosting
  • N. Oza, S. Russell
  • Computer Science
  • IEEE International Conference on Systems, Man and…
  • 2005
Bagging and boosting are two of the most well-known ensemble learning methods due to their theoretical performance guarantees and strong experimental results. However, these algorithms have been usedExpand
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Image Segmentation in Video Sequences: A Probabilistic Approach
"Background subtraction" is an old technique for finding moving objects in a video sequence--for example, cars driving on a freeway. The idea is that subtracting the current image from aExpand
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BLOG: Probabilistic Models with Unknown Objects
This paper introduces and illustrates BLOG, a formal language for defining probability models over worlds with unknown objects and identity uncertainty. BLOG unifies and extends several existingExpand
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Learning the Structure of Dynamic Probabilistic Networks
Dynamic probabilistic networks are a compact representation of complex stochastic processes. In this paper we examine how to learn the structure of a DPN from data. We extend structure scoring rulesExpand
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