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» A Preference Model for Structured Supervised Learning Tasks
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ICASSP
2010
IEEE
14 years 10 months ago
Learning sparse systems at sub-Nyquist rates: A frequency-domain approach
We propose a novel algorithm for sparse system identification in the frequency domain. Key to our result is the observation that the Fourier transform of the sparse impulse respo...
Martin McCormick, Yue M. Lu, Martin Vetterli
JMLR
2010
165views more  JMLR 2010»
14 years 4 months ago
Learning with Blocks: Composite Likelihood and Contrastive Divergence
Composite likelihood methods provide a wide spectrum of computationally efficient techniques for statistical tasks such as parameter estimation and model selection. In this paper,...
Arthur Asuncion, Qiang Liu, Alexander T. Ihler, Pa...
AIED
2009
Springer
15 years 4 months ago
Looking Into Collaborative Learning: Design from Macro- and Micro-Script Perspectives
Design of collaborative learning (CL) scenarios is a complex task, but necessary if the goal of the collaboration is learning. Creating well-thought-out CL scenarios requires exper...
Eloy D. Villasclaras-Fernández, Seiji Isota...
CIKM
2010
Springer
14 years 7 months ago
Regularization and feature selection for networked features
In the standard formalization of supervised learning problems, a datum is represented as a vector of features without prior knowledge about relationships among features. However, ...
Hongliang Fei, Brian Quanz, Jun Huan
AMS
2007
Springer
247views Robotics» more  AMS 2007»
15 years 4 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