Probabilistic inductive logic programming : theory and applications
"The question, how to combine probability and logic with learning, is gaining increased attention in several disciplines, e.g., knowledge representation, reasoning about uncertainty, data mining, and machine learning. The emerging field of study is 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."--Jacket
LNCS sublibrary, 4911
SpringerLink
Congress
1 online resource (viii, 339 pages) : illustrations
9783540786528, 9783540786511, 354078652X, 3540786511
233973998
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
English
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