Probabilistic Inductive Logic ProgrammingLuc De Raedt, Paolo Frasconi, Kristian Kersting, Stephen H. Muggleton Springer, 26.02.2008 - 341 Seiten This book provides an introduction to probabilistic inductive logic programming. It places emphasis on the methods based on logic programming principles and covers formalisms and systems, implementations and applications, as well as theory. |
Inhalt
1 | |
Relational Sequence Learning | 28 |
Learning with Kernels and Logical Representations | 56 |
Markov Logic | 92 |
New Advances in LogicBased Probabilistic Modeling by PRISM | 118 |
Constraint Logic Programming for Probabilistic Knowledge | 156 |
Basic Principles of Learning Bayesian Logic Programs | 189 |
The Independent Choice Logic and Beyond | 222 |
Protein Fold Discovery Using Stochastic Logic Programs | 244 |
Probabilistic Logic Learning from Haplotype Data | 263 |
Model Revision from Temporal Logic Properties in Computational Systems Biology | 287 |
A Behavioral Comparison of Some Probabilistic Logic Models | 305 |
ModelTheoretic Expressivity Analysis | 325 |
340 | |
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abstract acyclic algorithm alignment approach Artificial Intelligence background knowledge Bayes Bayesian clause Bayesian logic programs Bayesian networks BLPs classification CLP(BN conditional probability distribution conditional random fields Conference on Artificial constraints corresponding database datasets defined definition denote domain encode estimation example finite first-order logic formulas framework function given gradient graph ground atoms haplotype haplotype pair Heidelberg Herbrand hidden Markov models HMMs inductive logic programming inference intensional interpretations kernel Kersting kFOIL language LNCS LNAI Machine Learning markers Markov logic Markov logic networks Markov networks methods Muggleton multi-class node parameters PCFGs possible worlds predicate PRMs probabilistic ILP probabilistic logic probabilistic models problem Proceedings Prolog proof trees propositional protein fold query Raedt random variables relational models represent representation sample score semantics Skolem terms SLPs specific Springer stochastic logic programs structure symbols true values WEE1