Ádamo Lima Santana, Carlos Renato Francês, João Weyl Costa

Hybrid Strategies for Improving Bayesian Networks

Applying Mathematical and Computational Intelligence Models to Optimize and Extend the Modelling and Applicability

• Broschiertes Buch

Produktbeschreibung

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.

Produktdetails

• Verlag: LAP Lambert Academic Publishing
• Seitenzahl: 80
• Erscheinungstermin: 7. April 2011
• Englisch
• Abmessung: 220mm x 150mm x 5mm
• Gewicht: 137g
• ISBN-13: 9783844323146
• ISBN-10: 3844323147
• Artikelnr.: 33270923

Autorenporträt

Doctors Ádamo Santana, Carlos Francês and João Costa areresearchers at the Federal University of Pará, with vastexperience in applied computer science on the subjects ofcomputational intelligence and optimization; actively publishingin the domains of power systems and communication networks,particularly toward digital inclusion in Brazil.