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An inductive logic programming approach to statistical relational learning / Kristian Kersting.

By: Material type: TextTextSeries: Frontiers in artificial intelligence and applications ; v. 148. | Frontiers in artificial intelligence and applications. Dissertations in artificial intelligence.Publication details: Amsterdam ; Washington, D.C. : IOS Press, ©2006.Description: 1 online resource (xxii, 228 pages) : illustrationsContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 1586036742
  • 9781586036744
  • 9781429455275
  • 1429455276
  • 1433701243
  • 9781433701245
  • 9781607502074
  • 1607502070
Subject(s): Genre/Form: Additional physical formats: Print version:: Inductive logic programming approach to statistical relational learning.DDC classification:
  • 005.1/15 22
LOC classification:
  • QA76.63 .K47 2006eb
Online resources:
Contents:
Title page; Contents; Abstract; Overture; Part I: Probabilistic ILP over Interpretations; Part II: Probabilistic ILP over Time; Intermezzo: Exploiting Probabilistic ILP in Discriminative Classifiers; Part III: Making Complex Decisions in Relational Domains; Finale; Appendix; Bibliography; Symbol Index; Index.
Dissertation note: Ph. D. Albert-Ludwigs-Universität Freiburg im Breisgau 2006 Summary: Talks about Logic Programming, Uncertainty Reasoning and Machine Learning. This book includes definitions that circumscribe the area formed by extending Inductive Logic Programming to cases annotated with probability values. It investigates the approach of Learning from proofs and the issue of upgrading Fisher Kernels to Relational Fisher Kernels.
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Ph. D. Albert-Ludwigs-Universität Freiburg im Breisgau 2006

Includes bibliographical references (pages 201-221) and index.

Title page; Contents; Abstract; Overture; Part I: Probabilistic ILP over Interpretations; Part II: Probabilistic ILP over Time; Intermezzo: Exploiting Probabilistic ILP in Discriminative Classifiers; Part III: Making Complex Decisions in Relational Domains; Finale; Appendix; Bibliography; Symbol Index; Index.

Talks about Logic Programming, Uncertainty Reasoning and Machine Learning. This book includes definitions that circumscribe the area formed by extending Inductive Logic Programming to cases annotated with probability values. It investigates the approach of Learning from proofs and the issue of upgrading Fisher Kernels to Relational Fisher Kernels.

Print version record.

Master record variable field(s) change: 650

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