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GECCO
2011
Springer
276views Optimization» more  GECCO 2011»
14 years 3 months ago
Evolution of reward functions for reinforcement learning
The reward functions that drive reinforcement learning systems are generally derived directly from the descriptions of the problems that the systems are being used to solve. In so...
Scott Niekum, Lee Spector, Andrew G. Barto
WWW
2008
ACM
16 years 13 days ago
Improving web spam detection with re-extracted features
Web spam detection has become one of the top challenges for the Internet search industry. Instead of using some heuristic rules, we propose a feature re-extraction strategy to opt...
Guanggang Geng, Chunheng Wang, Qiudan Li
94
Voted
SIGIR
2010
ACM
15 years 3 months ago
Adaptive near-duplicate detection via similarity learning
In this paper, we present a novel near-duplicate document detection method that can easily be tuned for a particular domain. Our method represents each document as a real-valued s...
Hannaneh Hajishirzi, Wen-tau Yih, Aleksander Kolcz
ICRA
2009
IEEE
179views Robotics» more  ICRA 2009»
15 years 6 months ago
Automatic weight learning for multiple data sources when learning from demonstration
— Traditional approaches to programming robots are generally inaccessible to non-robotics-experts. A promising exception is the Learning from Demonstration paradigm. Here a polic...
Brenna Argall, Brett Browning, Manuela M. Veloso
ACL
2009
14 years 9 months ago
A Graph-based Semi-Supervised Learning for Question-Answering
We present a graph-based semi-supervised learning for the question-answering (QA) task for ranking candidate sentences. Using textual entailment analysis, we obtain entailment sco...
Asli Çelikyilmaz, Marcus Thint, Zhiheng Hua...