Title:
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Incremental methods for Bayesian network learning
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Author:
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Roure Alcobé, Josep; Sangüesa i Sole, Ramon
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Other authors:
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Universitat Politècnica de Catalunya. Departament de Ciències de la Computació; Universitat Politècnica de Catalunya. KEMLG - Grup d'Enginyeria del Coneixement i Aprenentatge Automàtic |
Abstract:
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Current methods for learning Bayesian Networks are mainly batch methods. That is, they are supposed to act in a single step
over the complete set of data. We remark the need to develop new approaches that do not require this to happen. Incremental
methods do proceed on the supposition that information is fed to the algorithm in a step by step fashion. We propose a
formalization for incremental methods, compare it to the most used one in other areas of machine learning and spot several
specific peculiarities of Bayesian networks. Present incremental methods are reviewed and criticized in terms of the problems
they present for dealing with order effects, and varying sizes of partial data sets. Finally we present BANDOLER a new
framework for learning Bayesian Networks incrementally. |
Subject(s):
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-Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial -Bayesian networks learning -BANDOLER -Incremental methods -Machine learning |
Rights:
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Document type:
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Article - Published version Report |
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