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» Learning Bounds for Domain Adaptation
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AMS
2007
Springer
247views Robotics» more  AMS 2007»
15 years 11 months ago
Towards Machine Learning of Motor Skills
Autonomous robots that can adapt to novel situations has been a long standing vision of robotics, artificial intelligence, and cognitive sciences. Early approaches to this goal du...
Jan Peters, Stefan Schaal, Bernhard Schölkopf
AAAI
1993
15 years 6 months ago
Complexity Analysis of Real-Time Reinforcement Learning
This paper analyzes the complexity of on-line reinforcement learning algorithms, namely asynchronous realtime versions of Q-learning and value-iteration, applied to the problem of...
Sven Koenig, Reid G. Simmons
CVPR
2012
IEEE
13 years 7 months ago
Learning object class detectors from weakly annotated video
Object detectors are typically trained on a large set of still images annotated by bounding-boxes. This paper introduces an approach for learning object detectors from realworld w...
Alessandro Prest, Christian Leistner, Javier Civer...
ECCV
2008
Springer
16 years 7 months ago
Training Hierarchical Feed-Forward Visual Recognition Models Using Transfer Learning from Pseudo-Tasks
Abstract. Building visual recognition models that adapt across different domains is a challenging task for computer vision. While feature-learning machines in the form of hierarchi...
Amr Ahmed, Kai Yu, Wei Xu, Yihong Gong, Eric P. Xi...
AAAI
2008
15 years 7 months ago
Efficient Learning of Action Schemas and Web-Service Descriptions
This work addresses the problem of efficiently learning action schemas using a bounded number of samples (interactions with the environment). We consider schemas in two languages-...
Thomas J. Walsh, Michael L. Littman