An Inductive Logic Programming Approach to Statistical Relational Learning

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IOS Press, 2006 - Computers - 228 pages
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"In his publication, the author Kristian Kersting has made an assault on one of the hardest integration problems at the heart of Artificial Intelligence research. This involves taking three disparate major areas of research and attempting a fusion among them. The three areas are: Logic Programming, Uncertainty Reasoning and Machine Learning. Every one of these is a major sub-area of research with its own associated international research conferences. Having taken on such a Herculean task, Kersting has produced a series of results which are now at the core of a newly emerging area: Probabilistic Inductive Logic Programming. The new area is closely tied to, though strictly subsumes, a new field known as Statistical Relational Learning which has in the last few years gained major prominence in the American Artificial Intelligence research community. Within this book, the author makes several major contributions, including the introduction of a series of definitions which circumscribe the new area formed by extending Inductive Logic Programming to the case in which clauses are annotated with probability values. Also, Kersting investigates the approach of Learning from proofs and the issue of upgrading Fisher Kernels to Relational Fisher kernels."
  

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Contents

Abstract
1
Probabilistic ILP over Interpretations
35
Learning Bayesian Logic Programs
54
Balios The Engine for Bayesian Logic Programs
77
Logical Hidden Markov Models
91
Three Basic Inference Problems for Logical HMMs
100
Learning the Structure of Logical HMMs
116
Exploiting Probabilistic ILP in Discriminative Classifiers
133
Making Complex Decisions in Relational Domains
149
Future Work
180
Conclusions
188
Bibliography
201
Symbol Index
222
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