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ICML
2006
IEEE
14 years 10 months ago
An analytic solution to discrete Bayesian reinforcement learning
Reinforcement learning (RL) was originally proposed as a framework to allow agents to learn in an online fashion as they interact with their environment. Existing RL algorithms co...
Pascal Poupart, Nikos A. Vlassis, Jesse Hoey, Kevi...
ICML
1999
IEEE
14 years 10 months ago
Using Reinforcement Learning to Spider the Web Efficiently
Consider the task of exploring the Web in order to find pages of a particular kind or on a particular topic. This task arises in the construction of search engines and Web knowled...
Jason Rennie, Andrew McCallum
ECOOP
2009
Springer
14 years 10 months ago
Making Sense of Large Heaps
Abstract. It is common for large-scale Java applications to suffer memory problems, whether inefficient designs that impede scalability, or lifetime bugs such as leaks. Making sens...
Nick Mitchell, Edith Schonberg, Gary Sevitsky
SAS
2009
Springer
171views Formal Methods» more  SAS 2009»
14 years 10 months ago
Bottom-Up Shape Analysis
In this paper we present a new shape analysis algorithm. The key distinguishing aspect of our algorithm is that it is completely compositional, bottom-up and non-iterative. We pres...
Bhargav S. Gulavani, Supratik Chakraborty, Ganesan...
KDD
2007
ACM
190views Data Mining» more  KDD 2007»
14 years 10 months ago
Model-shared subspace boosting for multi-label classification
Typical approaches to multi-label classification problem require learning an independent classifier for every label from all the examples and features. This can become a computati...
Rong Yan, Jelena Tesic, John R. Smith