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ICCV
2005
IEEE
16 years 1 months ago
Contour-Based Learning for Object Detection
We present a novel categorical object detection scheme that uses only local contour-based features. A two-stage, partially supervised learning architecture is proposed: a rudiment...
Jamie Shotton, Andrew Blake, Roberto Cipolla
AUSAI
2005
Springer
16 years 1 months ago
Global Versus Local Constructive Function Approximation for On-Line Reinforcement Learning
: In order to scale to problems with large or continuous state-spaces, reinforcement learning algorithms need to be combined with function approximation techniques. The majority of...
Peter Vamplew, Robert Ollington
EWLR
1997
Springer
15 years 11 months ago
Learning Complex Robot Behaviours by Evolutionary Computing with Task Decomposition
Building robots can be a tough job because the designer has to predict the interactions between the robot and the environment as well as to deal with them. One solution to cope the...
Wei-Po Lee, John Hallam, Henrik Hautop Lund
ECAI
2000
Springer
15 years 11 months ago
Learning Efficiently with Neural Networks: A Theoretical Comparison between Structured and Flat Representations
Abstract. We are interested in the relationship between learning efficiency and representation in the case of supervised neural networks for pattern classification trained by conti...
Marco Gori, Paolo Frasconi, Alessandro Sperduti
AAAI
2007
15 years 10 months ago
Measuring the Level of Transfer Learning by an AP Physics Problem-Solver
Transfer learning is the ability of an agent to apply knowledge learned in previous tasks to new problems or domains. We approach this problem by focusing on model formulation, i....
Matthew Klenk, Kenneth D. Forbus