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GECCO
2009
Springer
162views Optimization» more  GECCO 2009»
14 years 11 months ago
Uncertainty handling CMA-ES for reinforcement learning
The covariance matrix adaptation evolution strategy (CMAES) has proven to be a powerful method for reinforcement learning (RL). Recently, the CMA-ES has been augmented with an ada...
Verena Heidrich-Meisner, Christian Igel
JDCTA
2010
160views more  JDCTA 2010»
14 years 8 months ago
Learning and Decision Making in Human During a Game of Matching Pennies
To gain insights into the neural basis of such adaptive decision-making processes, we investigated the nature of learning process in humans playing a competitive game with binary ...
Jianfeng Hu, Xiaofeng Li, Jinghai Yin
JMLR
2010
136views more  JMLR 2010»
14 years 8 months ago
Reducing Label Complexity by Learning From Bags
We consider a supervised learning setting in which the main cost of learning is the number of training labels and one can obtain a single label for a bag of examples, indicating o...
Sivan Sabato, Nathan Srebro, Naftali Tishby
112
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ICDM
2010
IEEE
127views Data Mining» more  ICDM 2010»
14 years 12 months ago
Learning Markov Network Structure with Decision Trees
Traditional Markov network structure learning algorithms perform a search for globally useful features. However, these algorithms are often slow and prone to finding local optima d...
Daniel Lowd, Jesse Davis
UIST
2010
ACM
14 years 11 months ago
Gestalt: integrated support for implementation and analysis in machine learning
We present Gestalt, a development environment designed to support the process of applying machine learning. While traditional programming environments focus on source code, we exp...
Kayur Patel, Naomi Bancroft, Steven M. Drucker, Ja...