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» The Computational Complexity of Probabilistic Planning
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AAAI
2010
14 years 6 days ago
Integrating Sample-Based Planning and Model-Based Reinforcement Learning
Recent advancements in model-based reinforcement learning have shown that the dynamics of many structured domains (e.g. DBNs) can be learned with tractable sample complexity, desp...
Thomas J. Walsh, Sergiu Goschin, Michael L. Littma...
IEEEICCI
2003
IEEE
14 years 4 months ago
Signal Classification through Multifractal Analysis and Complex Domain Neural Networks
This paper describes a system capable of classifying stochastic, self-affine, nonstationary signals produced by nonlinear systems. The classification and analysis of these signals...
Witold Kinsner, V. Cheung, K. Cannons, J. Pear, T....
IROS
2009
IEEE
125views Robotics» more  IROS 2009»
14 years 5 months ago
Planning and fast re-planning of safe motions for humanoid robots: Application to a kicking motion
Abstract— Optimal motions are usually used as joint reference trajectories for repetitive or complex motions. In the case of soccer robots, the kicking motion is usually a benchm...
Sebastien Lengagne, Philippe Fraisse, Nacim Ramdan...
ICDCS
2009
IEEE
14 years 8 months ago
Modeling Probabilistic Measurement Correlations for Problem Determination in Large-Scale Distributed Systems
With the growing complexity in computer systems, it has been a real challenge to detect and diagnose problems in today’s large-scale distributed systems. Usually, the correlatio...
Jing Gao, Guofei Jiang, Haifeng Chen, Jiawei Han
CVPR
2003
IEEE
15 years 23 days ago
Video-Based Face Recognition Using Probabilistic Appearance Manifolds
This paper presents a novel method to model and recognize human faces in video sequences. Each registered person is represented by a low-dimensional appearance manifold in the amb...
Kuang-Chih Lee, Jeffrey Ho, Ming-Hsuan Yang, David...