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» TRUST-TECH based Methods for Optimization and Learning
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ICRA
2010
IEEE
143views Robotics» more  ICRA 2010»
14 years 11 months ago
Apprenticeship learning via soft local homomorphisms
Abstract— We consider the problem of apprenticeship learning when the expert’s demonstration covers only a small part of a large state space. Inverse Reinforcement Learning (IR...
Abdeslam Boularias, Brahim Chaib-draa
112
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ML
2008
ACM
101views Machine Learning» more  ML 2008»
15 years 20 days ago
On reoptimizing multi-class classifiers
Significant changes in the instance distribution or associated cost function of a learning problem require one to reoptimize a previously-learned classifier to work under new cond...
Chris Bourke, Kun Deng, Stephen D. Scott, Robert E...
144
Voted
CVPR
2009
IEEE
16 years 8 months ago
Learning sign language by watching TV (using weakly aligned subtitles)
The goal of this work is to automatically learn a large number of British Sign Language (BSL) signs from TV broadcasts. We achieve this by using the supervisory information avai...
Patrick Buehler (University of Oxford), Mark Everi...
100
Voted
ICCV
2003
IEEE
16 years 2 months ago
Learning How to Inpaint from Global Image Statistics
Inpainting is the problem of filling-in holes in images. Considerable progress has been made by techniques that use the immediate boundary of the hole and some prior information o...
Anat Levin, Assaf Zomet, Yair Weiss
ICML
2004
IEEE
16 years 1 months ago
Learning large margin classifiers locally and globally
A new large margin classifier, named MaxiMin Margin Machine (M4 ) is proposed in this paper. This new classifier is constructed based on both a "local" and a "globa...
Kaizhu Huang, Haiqin Yang, Irwin King, Michael R. ...