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ICML
2004
IEEE
16 years 7 months ago
Using relative novelty to identify useful temporal abstractions in reinforcement learning
lative Novelty to Identify Useful Temporal Abstractions in Reinforcement Learning ?Ozg?ur S?im?sek ozgur@cs.umass.edu Andrew G. Barto barto@cs.umass.edu Department of Computer Scie...
Özgür Simsek, Andrew G. Barto
ICML
2001
IEEE
16 years 7 months ago
Toward Optimal Active Learning through Sampling Estimation of Error Reduction
This paper presents an active learning method that directly optimizes expected future error. This is in contrast to many other popular techniques that instead aim to reduce versio...
Nicholas Roy, Andrew McCallum
EDUTAINMENT
2007
Springer
16 years 20 days ago
Method of Motion Data Processing Based on Manifold Learning
Due to the high-dimensionality of motion captured data which resulted in the complexity in motion analysis, a method of motion data processing based on manifold learning was propos...
Fengxia Li, Tianyu Huang, Lijie Li
ICRA
2005
IEEE
159views Robotics» more  ICRA 2005»
16 years 3 days ago
Learning Sensory Feedback to CPG with Policy Gradient for Biped Locomotion
— This paper proposes a learning framework for a CPG-based biped locomotion controller using a policy gradient method. Our goal in this study is to develop an efficient learning...
Takamitsu Matsubara, Jun Morimoto, Jun Nakanishi, ...
CIVR
2005
Springer
123views Image Analysis» more  CIVR 2005»
16 years 1 days ago
Region-Based Image Clustering and Retrieval Using Multiple Instance Learning
Multiple Instance Learning (MIL) is a special kind of supervised learning problem that has been studied actively in recent years. We propose an approach based on One-Class Support ...
Chengcui Zhang, Xin Chen