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» Learning the Dimensionality of Hidden Variables
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NIPS
1993
14 years 6 days ago
The Parti-Game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-Spaces
Parti-game is a new algorithm for learning feasible trajectories to goal regions in high dimensionalcontinuousstate-spaces. In high dimensions it is essential that learningdoes not...
Andrew W. Moore
ICRA
2007
IEEE
189views Robotics» more  ICRA 2007»
14 years 5 months ago
Context Estimation and Learning Control through Latent Variable Extraction: From discrete to continuous contexts
— Recent advances in machine learning and adaptive motor control have enabled efficient techniques for online learning of stationary plant dynamics and it’s use for robust pre...
Georgios Petkos, Sethu Vijayakumar
CVPR
2006
IEEE
15 years 27 days ago
Escaping local minima through hierarchical model selection: Automatic object discovery, segmentation, and tracking in video
Recently, the generative modeling approach to video segmentation has been gaining popularity in the computer vision community. For example, the flexible sprites framework has been...
Nebojsa Jojic, John M. Winn, Larry Zitnick
ICASSP
2010
IEEE
13 years 11 months ago
Robust regression using sparse learning for high dimensional parameter estimation problems
Algorithms such as Least Median of Squares (LMedS) and Random Sample Consensus (RANSAC) have been very successful for low-dimensional robust regression problems. However, the comb...
Kaushik Mitra, Ashok Veeraraghavan, Rama Chellappa
PPSN
2010
Springer
13 years 9 months ago
Evolving a Single Scalable Controller for an Octopus Arm with a Variable Number of Segments
Abstract. While traditional approaches to machine learning are sensitive to highdimensional state and action spaces, this paper demonstrates how an indirectly encoded neurocontroll...
Brian G. Woolley, Kenneth O. Stanley