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AAAI
2006
13 years 10 months ago
Anytime Induction of Decision Trees: An Iterative Improvement Approach
Most existing decision tree inducers are very fast due to their greedy approach. In many real-life applications, however, we are willing to allocate more time to get better decisi...
Saher Esmeir, Shaul Markovitch
SIGIR
2008
ACM
13 years 8 months ago
Optimizing relevance and revenue in ad search: a query substitution approach
The primary business model behind Web search is based on textual advertising, where contextually relevant ads are displayed alongside search results. We address the problem of sel...
Filip Radlinski, Andrei Z. Broder, Peter Ciccolo, ...
EDBT
2012
ACM
291views Database» more  EDBT 2012»
11 years 11 months ago
Relevance search in heterogeneous networks
Conventional research on similarity search focuses on measuring the similarity between objects with the same type. However, in many real-world applications, we need to measure the...
Chuan Shi, Xiangnan Kong, Philip S. Yu, Sihong Xie...
ATAL
2005
Springer
14 years 2 months ago
Improving reinforcement learning function approximators via neuroevolution
Reinforcement learning problems are commonly tackled with temporal difference methods, which use dynamic programming and statistical sampling to estimate the long-term value of ta...
Shimon Whiteson
ROBOCUP
2007
Springer
153views Robotics» more  ROBOCUP 2007»
14 years 2 months ago
Model-Based Reinforcement Learning in a Complex Domain
Reinforcement learning is a paradigm under which an agent seeks to improve its policy by making learning updates based on the experiences it gathers through interaction with the en...
Shivaram Kalyanakrishnan, Peter Stone, Yaxin Liu