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WACV
2005
IEEE
16 years 2 days ago
Using Co-Occurrence and Segmentation to Learn Feature-Based Object Models from Video
A number of recent systems for unsupervised featurebased learning of object models take advantage of cooccurrence: broadly, they search for clusters of discriminative features tha...
Thomas S. Stepleton, Tai Sing Lee
ITS
2004
Springer
138views Multimedia» more  ITS 2004»
15 years 12 months ago
A Metacognitive ACT-R Model of Students' Learning Strategies in Intelligent Tutoring Systems
Research has shown that students’ problem-solving actions vary in type and duration. Among other causes, this behavior is a result of strategies that are driven by different goal...
Ido Roll, Ryan Shaun Baker, Vincent Aleven, Kennet...
TNN
1998
112views more  TNN 1998»
15 years 6 months ago
A class of competitive learning models which avoids neuron underutilization problem
— In this paper, we study a qualitative property of a class of competitive learning (CL) models, which is called the multiplicatively biased competitive learning (MBCL) model, na...
Clifford Sze-Tsan Choy, Wan-Chi Siu
ICONIP
2010
15 years 5 months ago
Learning Basis Representations of Inverse Dynamics Models for Real-Time Adaptive Control
Abstract. In this paper, we propose a novel approach for adaptive control of robotic manipulators. Our approach uses a representation of inverse dynamics models learned from a vari...
Yasuhito Horiguchi, Takamitsu Matsubara, Masatsugu...
ECIR
2011
Springer
14 years 10 months ago
Learning Models for Ranking Aggregates
Aggregate ranking tasks are those where documents are not the final ranking outcome, but instead an intermediary component. For instance, in expert search, a ranking of candidate ...
Craig Macdonald, Iadh Ounis