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ISIPTA
2003
IEEE
125views Mathematics» more  ISIPTA 2003»
9 years 7 months ago
Game-Theoretic Learning Using the Imprecise Dirichlet Model
We discuss two approaches for choosing a strategy in a two-player game. We suppose that the game is played a large number of rounds, which allows the players to use observations o...
Erik Quaeghebeur, Gert de Cooman
ICPR
2008
IEEE
9 years 8 months ago
Improving Bayesian Network parameter learning using constraints
This paper describes a new approach to unify constraints on parameters with training data to perform parameter estimation in Bayesian networks of known structure. The method is ge...
Cassio Polpo de Campos, Qiang Ji
IDA
2007
Springer
9 years 1 months ago
Second-order uncertainty calculations by using the imprecise Dirichlet model
Natural extension is a powerful tool for combining the expert judgments in the framework of imprecise probability theory. However, it assumes that every judgment is “true” and...
Lev V. Utkin
ICDM
2009
IEEE
111views Data Mining» more  ICDM 2009»
9 years 8 months ago
A Game Theoretical Model for Adversarial Learning
Abstract—It is now widely accepted that in many situations where classifiers are deployed, adversaries deliberately manipulate data in order to reduce the classifier’s accura...
Wei Liu, Sanjay Chawla
ICNC
2005
Springer
9 years 7 months ago
A Game-Theoretic Approach to Competitive Learning in Self-Organizing Maps
Abstract. Self-Organizing Maps (SOM) is a powerful tool for clustering and discovering patterns in data. Competitive learning in the SOM training process focusses on finding a neu...
Joseph P. Herbert, Jingtao Yao
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