Hybrid Random Fields A Scalable Approach to Structure and Parameter Learning in Probabilistic Graphical Models /

• Κύριοι συγγραφείς: Freno, Antonino (Συγγραφέας), Trentin, Edmondo (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg, 2011.
• Σειρά: Intelligent Systems Reference Library, 15
• Θέματα: Engineering.; Artificial intelligence.; Computational intelligence.; Computational Intelligence.; Artificial Intelligence (incl. Robotics).

This book presents an exciting new synthesis of directed and undirected, discrete and continuous graphical models. Combining elements of Bayesian networks and Markov random fields, the newly introduced hybrid random fields are an interesting approach to get the best of both these worlds, with an add...

Πίνακας περιεχομένων:

• Introduction
• Bayesian Networks
• Markov Random Fields
• Introducing Hybrid Random Fields: Discrete-Valued Variables
• Extending Hybrid Random Fields: Continuous-Valued Variables
• Applications
• Probabilistic Graphical Models: Cognitive Science or Cognitive Technology? .
• Conclusions.