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» Compositional Models for Reinforcement Learning
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ICPR
2006
IEEE
14 years 8 months ago
Learning Mixtures of Offline and Online features for Handwritten Stroke Recognition
In this paper we propose a novel scheme to combine offline and online features of handwritten strokes. The stateof-the-art methods in handwritten stroke recognition have used a pr...
C. V. Jawahar, Karteek Alahari, Satya Lahari Putre...
ICWS
2004
IEEE
13 years 9 months ago
Dynamic Workflow Composition using Markov Decision Processes
The advent of Web services has made automated workflow composition relevant to Web based applications. One technique that has received some attention, for automatically composing ...
Prashant Doshi, Richard Goodwin, Rama Akkiraju, Ku...
NIPS
1992
13 years 9 months ago
Explanation-Based Neural Network Learning for Robot Control
How can artificial neural nets generalize better from fewer examples? In order to generalize successfully, neural network learning methods typically require large training data se...
Tom M. Mitchell, Sebastian Thrun
IROS
2009
IEEE
205views Robotics» more  IROS 2009»
14 years 2 months ago
Probabilistic categorization of kitchen objects in table settings with a composite sensor
— In this paper, we investigate the problem of 3D object categorization of objects typically present in kitchen environments, from data acquired using a composite sensor. Our fra...
Zoltan Csaba Marton, Radu Bogdan Rusu, Dominik Jai...
ICGI
1998
Springer
13 years 12 months ago
Meaning Helps Learning Syntax
In this paper, we propose a new framework for the computational learning of formal grammars with positive data. In this model, both syntactic and semantic information are taken int...
Isabelle Tellier