Probabilistic Inductive Logic Programming Theory and Applications /

• Corporate Author: SpringerLink (Online service)
• Other Authors: Raedt, Luc De (Editor), Frasconi, Paolo (Editor), Kersting, Kristian (Editor), Muggleton, Stephen (Editor)
• Format: Electronic eBook
• Language: English
• Published: Berlin, Heidelberg : Springer Berlin Heidelberg, 2008.
• Series: Lecture Notes in Computer Science, 4911
• Subjects: Computer science.; Computer programming.; Algorithms.; Mathematical logic.; Data mining.; Artificial intelligence.; Bioinformatics.; Computer Science.; Artificial Intelligence (incl. Robotics).; Programming Techniques.; Mathematical Logic and Formal Languages.; Algorithm Analysis and Problem Complexity.; Data Mining and Knowledge Discovery.; Computational Biology/Bioinformatics.

Table of 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.