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» A Minimax Method for Learning Functional Networks
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ICML
2006
IEEE
14 years 6 months ago
A DC-programming algorithm for kernel selection
We address the problem of learning a kernel for a given supervised learning task. Our approach consists in searching within the convex hull of a prescribed set of basic kernels fo...
Andreas Argyriou, Raphael Hauser, Charles A. Micch...
ECAI
2010
Springer
13 years 6 months ago
Case-Based Multiagent Reinforcement Learning: Cases as Heuristics for Selection of Actions
This work presents a new approach that allows the use of cases in a case base as heuristics to speed up Multiagent Reinforcement Learning algorithms, combining Case-Based Reasoning...
Reinaldo A. C. Bianchi, Ramon López de M&aa...
IJCAI
1997
13 years 6 months ago
An Effective Learning Method for Max-Min Neural Networks
Max and min operations have interesting properties that facilitate the exchange of information between the symbolic and real-valued domains. As such, neural networks that employ m...
Loo-Nin Teow, Kia-Fock Loe
ANOR
2002
88views more  ANOR 2002»
13 years 5 months ago
Multicriteria Semi-Obnoxious Network Location Problems (MSNLP) with Sum and Center Objectives
Locating a facility is often modeled as either the maxisum or the minisum problem, reflecting whether the facility is undesirable (obnoxious) or desirable. But many facilities are ...
Horst W. Hamacher, Martine Labbé, Stefan Ni...
BMCBI
2010
179views more  BMCBI 2010»
13 years 5 months ago
A semi-supervised learning approach to predict synthetic genetic interactions by combining functional and topological properties
Background: Genetic interaction profiles are highly informative and helpful for understanding the functional linkages between genes, and therefore have been extensively exploited ...
Zhuhong You, Zheng Yin, Kyungsook Han, De-Shuang H...