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IJAR
2010
105views more  IJAR 2010»
8 years 11 months ago
A tree augmented classifier based on Extreme Imprecise Dirichlet Model
In this paper we present TANC, i.e., a tree-augmented naive credal classifier based on imprecise probabilities; it models prior near-ignorance via the Extreme Imprecise Dirichlet ...
G. Corani, C. P. de Campos
IPMU
2010
Springer
8 years 11 months ago
Restricting the IDM for Classification
Abstract. The naive credal classifier (NCC) extends naive Bayes classifier (NBC) to imprecise probabilities to robustly deal with the specification of the prior; NCC models a state...
Giorgio Corani, Alessio Benavoli
AMAI
2005
Springer
9 years 1 months ago
Robust inference of trees
Abstract. This paper is concerned with the reliable inference of optimal treeapproximations to the dependency structure of an unknown distribution generating data. The traditional ...
Marco Zaffalon, Marcus Hutter
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
ISIPTA
1999
IEEE
9 years 5 months ago
Implicative Analysis for Multivariate Binary Data using an Imprecise Dirichlet Model
Bayesian implicative analysis was proposed for summarizing the association in a 22 contingency table in terms possibly asymmetrical such as, e.g., presence of feature a implies, i...
Jean-Marc Bernard
ISIPTA
2003
IEEE
125views Mathematics» more  ISIPTA 2003»
9 years 6 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
ISIPTA
2005
IEEE
162views Mathematics» more  ISIPTA 2005»
9 years 7 months ago
Learning from multinomial data: a nonparametric predictive alternative to the Imprecise Dirichlet Model
A new model for learning from multinomial data has recently been developed, giving predictive inferences in the form of lower and upper probabilities for a future observation. Apa...
Frank P. A. Coolen, Thomas Augustin
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
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