Hybrid Strategies for Improving Bayesian Networks : Applying Mathematical and Computational Intelligence Models to Optimize and Extend the Modelling and Applicability

Language: English

Published by LAP LAMBERT Academic Publishing, 2011

3844323147 / 9783844323146

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Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH

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nach der Bestellung gedruckt Neuware - Printed after ordering - One of the main factors for the success of data mining is related to the comprehensibility of the patterns discovered by the computational intelligence techniques; with Bayesian networks standing as one of the most prominent, when considering the easiness of knowledge interpretation achieved. Its quantitative and qualitative semantics, allied to the comprehensibility of the patterns discovered, motivates its application in the knowledge discovery process. Bayesian networks, however, like any computational intelligence technique, presents limitations and disadvantages regarding its use; amongst which we can point the learning of the structure from large datasets and the provision of inferences throughout time. This book will show extensions for the improvement of Bayesian networks, presenting strategies to improve its properties, treating aspects such as performance, as well as interpretability and use of its results; incorporating models of multiple regression for structure learning, and temporal aspects using Markov chains. The models should help users extending the range of applicability of this versatile model for new domains and tasks.

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Title
Hybrid Strategies for Improving Bayesian Networks : Applying Mathematical and Computational Intelligence Models to Optimize and Extend the Modelling and Applicability
Author
Ádamo Lima Santana
Publisher
LAP LAMBERT Academic Publishing
Publication year
2011
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
3844323147
ISBN 13
9783844323146
Item weight
137 grams
Dimensions
220x150x6 mm

AHA-BUCH GmbH

Einbeck, Germany

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