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» On learning algorithm selection for classification
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CVPR
2010
IEEE
16 years 2 months ago
Safety in Numbers: Learning Categories from Few Examples with Multi Model Knowledge Transfer
Learning object categories from small samples is a challenging problem, where machine learning tools can in general provide very few guarantees. Exploiting prior knowledge may be ...
Tatiana Tommasi, Francesco Orabona, Barbara Caputo
ICRA
2006
IEEE
161views Robotics» more  ICRA 2006»
16 years 17 days ago
Quadruped Robot Obstacle Negotiation via Reinforcement Learning
— Legged robots can, in principle, traverse a large variety of obstacles and terrains. In this paper, we describe a successful application of reinforcement learning to the proble...
Honglak Lee, Yirong Shen, Chih-Han Yu, Gurjeet Sin...
MM
2010
ACM
115views Multimedia» more  MM 2010»
15 years 5 months ago
Interactive learning of heterogeneous visual concepts with local features
In the context of computer-assisted plant identification we are facing challenging information retrieval problems because of the very high within-class variability and of the lim...
Wajih Ouertani, Michel Crucianu, Nozha Boujemaa
ACL
2009
15 years 4 months ago
Reinforcement Learning for Mapping Instructions to Actions
In this paper, we present a reinforcement learning approach for mapping natural language instructions to sequences of executable actions. We assume access to a reward function tha...
S. R. K. Branavan, Harr Chen, Luke S. Zettlemoyer,...
ML
2002
ACM
123views Machine Learning» more  ML 2002»
15 years 6 months ago
Feature Generation Using General Constructor Functions
Most classification algorithms receive as input a set of attributes of the classified objects. In many cases, however, the supplied set of attributes is not sufficient for creatin...
Shaul Markovitch, Dan Rosenstein