An inductive logic programming approach to statistical relational learning.1586036742 An inductive logic programming Inductive logic programming (ILP) is a subfield of machine learning which uses logic programming as a uniform representation for examples, background knowledge and hypotheses. approach to statistical relational learning. Kersting, Kristian. IOS (1) (Internetwork Operating System) An operating system from Cisco that is the primary control program used in its routers. IOS is widely used and robust system software that supports the common functions of all products under Cisco's CiscoFusion architecture. Press 2006 228 pages $131.00 Hardcover Frontiers in artificial intelligence and applications; v.148; Dissertations in artificial intelligence QA76.63 In artificial intelligence, statistical relational learning addresses the integration of probabilistic (probability) probabilistic - Relating to, or governed by, probability. The behaviour of a probabilistic system cannot be predicted exactly but the probability of certain behaviours is known. Such systems may be simulated using pseudorandom numbers. reasoning with first order logic representation and machine learning. In this treatise, Kersting (Institute for Computer Science, Albert-Ludwigs-U. Freiburg, Germany) develops a general framework of probabilistic inductive logic programming as a foundation for his approach to statistical relational learning, which incorporates the logical concepts of objects and relations among objects into Bayesian networks. Further, Bayesian networks are upgraded to Bayesian logic Bayesian logic A type of reasoning in which the likelihood of an event occurring can be described in quantitative—ie probabilistic terms. See Artificial intelligence, Computer-assisted diagnosis. programs, hidden Markov models to logical hidden Markov models, and Markov decision process Markov decision processes (MDPs) provide a mathematical framework for modeling decision-making in situations where outcomes are partly random and partly under the control of the decision maker. to Markov decision programs. Finally, he seeks to show that statistical relational learning approaches naturally yield kernels for structured data and demonstrates these approaches using examples from genetics, bio-informatics, and classical planning domains. ([c]20072005 Book News, Inc., Portland, OR) |
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