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» Evaluating learning algorithms and classifiers
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IJCAI
2007
15 years 2 months ago
Heuristic Selection of Actions in Multiagent Reinforcement Learning
This work presents a new algorithm, called Heuristically Accelerated Minimax-Q (HAMMQ), that allows the use of heuristics to speed up the wellknown Multiagent Reinforcement Learni...
Reinaldo A. C. Bianchi, Carlos H. C. Ribeiro, Anna...
101
Voted
AAAI
2008
15 years 3 months ago
Bounding the False Discovery Rate in Local Bayesian Network Learning
Modern Bayesian Network learning algorithms are timeefficient, scalable and produce high-quality models; these algorithms feature prominently in decision support model development...
Ioannis Tsamardinos, Laura E. Brown
81
Voted
ECML
2006
Springer
15 years 4 months ago
Why Is Rule Learning Optimistic and How to Correct It
Abstract. In their search through a huge space of possible hypotheses, rule induction algorithms compare estimations of qualities of a large number of rules to find the one that ap...
Martin Mozina, Janez Demsar, Jure Zabkar, Ivan Bra...
SSDBM
2008
IEEE
114views Database» more  SSDBM 2008»
15 years 7 months ago
A General Framework for Increasing the Robustness of PCA-Based Correlation Clustering Algorithms
Abstract. Most correlation clustering algorithms rely on principal component analysis (PCA) as a correlation analysis tool. The correlation of each cluster is learned by applying P...
Hans-Peter Kriegel, Peer Kröger, Erich Schube...
INFOVIS
2000
IEEE
15 years 5 months ago
Interactive Problem Solving via Algorithm Visualization
COMIND is a tool for conceptual design of industrial products. It helps designers define and evaluate the initial design space by using search algorithms to generate sets of feasi...
Pearl Pu, Denis Lalanne