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» Learning Generative Models with the Up-Propagation Algorithm
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JMLR
2010
103views more  JMLR 2010»
13 years 2 months ago
Learning Nonlinear Dynamic Models from Non-sequenced Data
Virtually all methods of learning dynamic systems from data start from the same basic assumption: the learning algorithm will be given a sequence of data generated from the dynami...
Tzu-Kuo Huang, Le Song, Jeff Schneider
PTS
2010
134views Hardware» more  PTS 2010»
13 years 6 months ago
A Learning-Based Approach to Unit Testing of Numerical Software
We present an application of learning-based testing to the problem of automated test case generation (ATCG) for numerical software. Our approach uses n-dimensional polynomial model...
Karl Meinke, Fei Niu
ITICSE
2004
ACM
14 years 1 months ago
Generation as method for explorative learning in computer science education
The use of generic and generative methods for the development and application of interactive educational software is a relatively unexplored area in industry and education. Advant...
Andreas Kerren
IJHIS
2006
94views more  IJHIS 2006»
13 years 7 months ago
A new fine-grained evolutionary algorithm based on cellular learning automata
In this paper, a new evolutionary computing model, called CLA-EC, is proposed. This model is a combination of a model called cellular learning automata (CLA) and the evolutionary ...
Reza Rastegar, Mohammad Reza Meybodi, Arash Hariri
PRICAI
2000
Springer
13 years 11 months ago
Generating Hierarchical Structure in Reinforcement Learning from State Variables
This paper presents the CQ algorithm which decomposes and solves a Markov Decision Process (MDP) by automatically generating a hierarchy of smaller MDPs using state variables. The ...
Bernhard Hengst