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Ajouter au panierEtat : Sehr gut. Zustand: Sehr gut | Seiten: 256 | Sprache: Englisch | Produktart: Bücher.
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Ajouter au panierPAP. Etat : New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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Ajouter au panierPaperback. Etat : New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 526.
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Ajouter au panierBuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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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Ajouter au panierEtat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 .