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Probabilistic Inductive Logic Programming
by L. de Raedt and Luc De Raedt and Paolo Frasconi

Overview -

The question, how to combine probability and logic with learning, is getting an increased attention in several disciplines such as knowledge representation, reasoning about uncertainty, data mining, and machine learning simulateously. This results in the newly emerging subfield known under the names of statistical relational learning and probabilistic inductive logic programming.  Read more...


 
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More About Probabilistic Inductive Logic Programming by L. de Raedt; Luc De Raedt; Paolo Frasconi
 
 
 
Overview

The question, how to combine probability and logic with learning, is getting an increased attention in several disciplines such as knowledge representation, reasoning about uncertainty, data mining, and machine learning simulateously. This results in the newly emerging subfield known under the names of statistical relational learning and probabilistic inductive logic programming.

This book provides an introduction to the field with an emphasis on the methods based on logic programming principles. It is concerned with formalisms and systems, implementations and applications, as well as with the theory of probabilistic inductive logic programming.

The 13 chapters of this state-of-the-art survey start with an introduction to probabilistic inductive logic programming; moreover the book presents a detailed overview of the most important probabilistic logic learning formalisms and systems such as relational sequence learning techniques, using kernels with logical representations, Markov logic, the PRISM system, CLP(BN), Bayesian logic programs, and the independent choice logic. The third part provides a detailed account of some show-case applications of probabilistic inductive logic programming. The final part touches upon some theoretical investigations and includes chapters on behavioural comparison of probabilistic logic programming representations and a model-theoretic expressivity analysis.

 
Details
  • ISBN-13: 9783540786511
  • ISBN-10: 3540786511
  • Publisher: Springer
  • Publish Date: June 2008
  • Page Count: 341

Series: Lecture Notes in Computer Science #860

Related Categories

Books > Computers & Internet > Intelligence (AI) & Semantics
Books > Computers & Internet > Programming - Algorithms
Books > Computers & Internet > Computer Science

 
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