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

Ádamo Lima Santana (u. a.)

ISBN 10: 3844323147 / ISBN 13: 9783844323146

Published by LAP LAMBERT Academic Publishing, 2011

About this Item

Hybrid Strategies for Improving Bayesian Networks | Applying Mathematical and Computational Intelligence Models to Optimize and Extend the Modelling and Applicability | Ádamo Lima Santana (u. a.) | Taschenbuch | 80 S. | Englisch | 2011 | LAP LAMBERT Academic Publishing | EAN 9783844323146 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

Seller Inventory # 107052260

Bibliographic Details

• Title: Hybrid Strategies for Improving Bayesian Networks | Applying Mathematical and Computational Intelligence Models to Optimize and Extend the Modelling and Applicability
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2011
• Language: English
• ISBN 10: 3844323147
• ISBN 13: 9783844323146
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 5D millimeters 220 millimeters width by 150 millimeters height by 5 millimeters depth
• Weight: 137 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

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.

About the Author

Doctors Ádamo Santana, Carlos Francęs and Joăo Costa are researchers at the Federal University of Pará, with vast experience in applied computer science on the subjects of computational intelligence and optimization; actively publishing in the domains of power systems and communication networks, particularly toward digital inclusion in Brazil.