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» Learning Rules from Distributed Data
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GECCO
2005
Springer
232views Optimization» more  GECCO 2005»
15 years 8 months ago
Factorial representations to generate arbitrary search distributions
A powerful approach to search is to try to learn a distribution of good solutions (in particular of the dependencies between their variables) and use this distribution as a basis ...
Marc Toussaint
132
Voted
ICML
2010
IEEE
15 years 3 months ago
Multiagent Inductive Learning: an Argumentation-based Approach
Multiagent Inductive Learning is the problem that groups of agents face when they want to perform inductive learning, but the data of interest is distributed among them. This pape...
Santiago Ontañón, Enric Plaza
145
Voted
PR
2011
14 years 5 months ago
A survey of multilinear subspace learning for tensor data
Increasingly large amount of multidimensional data are being generated on a daily basis in many applications. This leads to a strong demand for learning algorithms to extract usef...
Haiping Lu, Konstantinos N. Plataniotis, Anastasio...
113
Voted
IJAR
2002
98views more  IJAR 2002»
15 years 2 months ago
Contradiction sensitive fuzzy model-based adaptive control
Fuzzy model-based adaptive control, unlike traditional fuzzy control, extracts expert knowledge from data by using model identification techniques. In this paper, we propose an an...
Pablo Carmona, Juan Luis Castro, Jose Manuel Zurit...
152
Voted
IEEEPACT
2008
IEEE
15 years 9 months ago
Feature selection and policy optimization for distributed instruction placement using reinforcement learning
Communication overheads are one of the fundamental challenges in a multiprocessor system. As the number of processors on a chip increases, communication overheads and the distribu...
Katherine E. Coons, Behnam Robatmili, Matthew E. T...