Daniel Lowd (6 results)

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    • Language: English

      Published by Springer, 2009

      3031004213 / 9783031004216

      • Softcover

      Seller: Ria Christie Collections, Uxbridge, United KingdomRia Christie Collections

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      Condition: New. In English.

    • Language: English

      Published by Springer, 2009

      3031004213 / 9783031004216

      • Softcover

      Seller: Books Puddle, New York, NY, U.S.A.Books Puddle

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      Condition: New. 1st edition NO-PA16APR2015-KAP.

    • Language: English

      Published by Springer, 2009

      3031004213 / 9783031004216

      • Softcover
      • Print on Demand

      Seller: Majestic Books, Hounslow, United KingdomMajestic Books

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    • Language: English

      Published by Springer, 2009

      3031004213 / 9783031004216

      • Softcover
      • Print on Demand

      Seller: Biblios, frankfurt am main, HESSE, GermanyBiblios

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    • Language: English

      Published by Springer International Publishing Jun 2009, 2009

      3031004213 / 9783031004216

      • Softcover
      • Print on Demand

      Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.

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      Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Most subfields of computer science have an interface layer via which applications communicate with the infrastructure, and this is key to their success (e.g., the Internet in networking, the relational model in databases, etc.). So far this interface layer has been missing in AI. First-order logic and probabilistic graphical models each have some of the necessary features, but a viable interface layer requires combining both. Markov logic is a powerful new language that accomplishes this by attaching weights to first-order formulas and treating them as templates for features of Markov random fields. Most statistical models in wide use are special cases of Markov logic, and first-order logic is its infinite-weight limit. Inference algorithms for Markov logic combine ideas from satisfiability, Markov chain Monte Carlo, belief propagation, and resolution. Learning algorithms make use of conditional likelihood, convex optimization, and inductive logic programming. Markov logic has been successfully applied to problems in information extraction and integration, natural language processing, robot mapping, social networks, computational biology, and others, and is the basis of the open-source Alchemy system. Table of Contents: Introduction / Markov Logic / Inference / Learning / Extensions / Applications / Conclusion 156 pp. Englisch.

    • Language: English

      Published by Springer, Berlin|Springer International Publishing|Morgan & Claypool|Springer, 2009

      3031004213 / 9783031004216

      • Softcover
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      Seller: moluna, Greven, Germanymoluna

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      Kartoniert / Broschiert. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Most subfields of computer science have an interface layer via which applications communicate with the infrastructure, and this is key to their success (e.g., the Internet in networking, the relational model in databases, etc.). So far this interface layer .