An Inductive Logic Programming Approach to Statistical Relational Learning
IOS Press, 2006 - 228 Seiten
"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."
Was andere dazu sagen - Rezension schreiben
Es wurden keine Rezensionen gefunden.
Probabilistic ILP over Interpretations
Learning Bayesian Logic Programs
Balios The Engine for Bayesian Logic Programs
Logical Hidden Markov Models
Three Basic Inference Problems for Logical HMMs
Learning the Structure of Logical HMMs
abstract policies abstract transitions action algorithm approach Artificial Intelligence background knowledge Bayesian clauses Bayesian logic programs Bayesian networks blocks world cl(a cl(b combining rules compute conditional probability distributions constraints convergence covers relation defined definite clause denotes domain Dzeroski editors encodes evaluation examples Figure finite set Fisher kernels framework Furthermore gradient grammars ground atoms hidden Markov models hypothesis inductive logic programming inference instance Kersting learning Bayesian learning from entailment learning from interpretations least Herbrand model log-likelihood logical atoms logical hidden Markov logical HMMs Machine Learning Machine Learning Journal Markov decision processes Markov decision programs Markov logic networks Markov networks maximally MDPs Naive Bayes nodes on(a parameter estimation pc(ann probabilistic ILP probabilistic learning probabilistic relational models propositional Q-rules Raedt random variables refinement operators reinforcement learning relational Fisher kernels relational reinforcement learning represent representation SAGEM SCOOBY score sequence specifies statistical relational learning stochastic logic programs support network symbols tree value function