Probabilistic Inductive Logic Programming Theory and Applications
by
 
Raedt, Luc. editor.

Title
Probabilistic Inductive Logic Programming Theory and Applications

Author
Raedt, Luc. editor.

ISBN
9783540786528

Physical Description
VIII, 341 p. online resource.

Series
Lecture Notes in Computer Science, 4911

Contents
Probabilistic Inductive Logic Programming -- Formalisms and Systems -- Relational Sequence Learning -- Learning with Kernels and Logical Representations -- Markov Logic -- New Advances in Logic-Based Probabilistic Modeling by PRISM -- CLP( ): Constraint Logic Programming for Probabilistic Knowledge -- Basic Principles of Learning Bayesian Logic Programs -- The Independent Choice Logic and Beyond -- Applications -- Protein Fold Discovery Using Stochastic Logic Programs -- Probabilistic Logic Learning from Haplotype Data -- Model Revision from Temporal Logic Properties in Computational Systems Biology -- Theory -- A Behavioral Comparison of Some Probabilistic Logic Models -- Model-Theoretic Expressivity Analysis.

Subject Term
Computer science.
 
Computer software.
 
Data mining.
 
Artificial intelligence.
 
Bioinformatics.
 
Artificial Intelligence (incl. Robotics).
 
Programming Techniques.
 
Mathematical Logic and Formal Languages.
 
Algorithm Analysis and Problem Complexity.
 
Data Mining and Knowledge Discovery.
 
Computational Biology/Bioinformatics.

Added Author
Raedt, Luc.
 
Frasconi, Paolo.
 
Kersting, Kristian.
 
Muggleton, Stephen.

Added Corporate Author
SpringerLink (Online service)

Electronic Access
http://dx.doi.org/10.1007/978-3-540-78652-8


LibraryMaterial TypeItem BarcodeShelf NumberStatus
IYTE LibraryE-Book503409-1001Q334 -342Online Springer