Learning from imprecise data: possibilistic graphical models
نویسندگان
چکیده
منابع مشابه
Learning from Imprecise Data: Possibilistic Graphical Models
Graphical models—especially probabilistic networks like Bayes networks and Markov networks—are very popular to make reasoning in highdimensional domains feasible. Since constructing them manually can be tedious and time consuming, a large part of recent research has been devoted to learning them from data. However, if the dataset to learn from contains imprecise information in the form of sets ...
متن کاملLearning possibilistic graphical models from data
Graphical models—especially probabilistic networks like Bayes networks and Markov networks—are very popular to make reasoning in high-dimensional domains feasible. Since constructing them manually can be tedious and time consuming, a large part of recent research has been devoted to learning them from data. However, if the dataset to learn from contains imprecise information in the form of sets...
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There has been an ever-increasing interest in multi-disciplinary research on representing and reasoning with imperfect data. Possibilistic networks present one of the powerful frameworks of interest for representing uncertain and imprecise information. This paper covers the problem of their parameters learning from imprecise datasets, i.e., containing multi-valued data. We propose in the first ...
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Data Mining, also called Knowledge Discovery in Databases, is a young area of research, which has emerged in response to the flood of data we are faced with nowadays. It has taken up the challenge to develop techniques that can help humans discover useful patterns in their data. One such technique—which certainly is among the most important, as it can be used for frequent data mining tasks like...
متن کاملPossibilistic Graphical Models
Graphical modeling is an important method to efficiently represent and analyze uncertain information in knowledge-based systems. Its most prominent representatives are Bayesian networks and Markov networks for probabilistic reasoning, which have been well-known for over ten years now. However, they suffer from certain deficiencies, if imprecise information has to be taken into account. Therefor...
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ژورنال
عنوان ژورنال: Computational Statistics & Data Analysis
سال: 2002
ISSN: 0167-9473
DOI: 10.1016/s0167-9473(01)00071-8