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ICML
2004
IEEE
16 years 7 months ago
Learning and discovery of predictive state representations in dynamical systems with reset
Predictive state representations (PSRs) are a recently proposed way of modeling controlled dynamical systems. PSR-based models use predictions of observable outcomes of tests that...
Michael R. James, Satinder P. Singh
CVPR
2010
IEEE
16 years 2 months ago
Online Multiple Instance Learning with No Regret
Multiple instance (MI) learning is a recent learning paradigm that is more flexible than standard supervised learning algorithms in the handling of label ambiguity. It has been u...
Li Mu, James Kwok, Lu Bao-liang
175
Voted
ICRA
2009
IEEE
125views Robotics» more  ICRA 2009»
16 years 1 months ago
Learning motor primitives for robotics
— The acquisition and self-improvement of novel motor skills is among the most important problems in robotics. Motor primitives offer one of the most promising frameworks for the...
Jens Kober, Jan Peters
AIED
2007
Springer
16 years 1 months ago
Can a Polite Intelligent Tutoring System Lead to Improved Learning Outside of the Lab?
In this work we are investigating the learning benefits of e-Learning principles (a) within the context of a web-based intelligent tutor and (b) in the “wild,” that is, in real...
Bruce M. McLaren, Sung-Joo Lim, David Yaron, Kenne...
GECCO
2005
Springer
132views Optimization» more  GECCO 2005»
16 years 10 days ago
A statistical learning theory approach of bloat
Code bloat, the excessive increase of code size, is an important issue in Genetic Programming (GP). This paper proposes a theoretical analysis of code bloat in the framework of sy...
Sylvain Gelly, Olivier Teytaud, Nicolas Bredeche, ...