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INTRODUCTION TO STATISTICAL RELATIONAL LEARNING (ADAPTIVE By Lise Getoor & Ben - Picture 1 of 1
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INTRODUCTION TO STATISTICAL RELATIONAL LEARNING (ADAPTIVE By Lise Getoor & Ben

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US $56.95
Approximately£42.77
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    eBay item number:226671013557
    Last updated on 09 Apr, 2025 07:09:32 BSTView all revisionsView all revisions

    Item specifics

    Condition
    New: A new, unread, unused book in perfect condition with no missing or damaged pages. See the ...
    ISBN-10
    0262072882
    Book Title
    Introduction to Statistical Relational Learning (Adaptive
    Genre
    COMPUTERS
    ISBN
    9780262072885

    About this product

    Product Identifiers

    Publisher
    MIT Press
    ISBN-10
    0262072882
    ISBN-13
    9780262072885
    eBay Product ID (ePID)
    59783046

    Product Key Features

    Number of Pages
    608 Pages
    Language
    English
    Publication Name
    Introduction to Statistical Relational Learning
    Subject
    Programming / Algorithms, Intelligence (Ai) & Semantics, Databases / General, Logic Design
    Publication Year
    2007
    Type
    Textbook
    Author
    Ben Taskar
    Subject Area
    Computers
    Series
    Adaptive Computation and Machine Learning Ser.
    Format
    Hardcover

    Dimensions

    Item Height
    1 in
    Item Weight
    44.9 Oz
    Item Length
    10 in
    Item Width
    8 in

    Additional Product Features

    Intended Audience
    Scholarly & Professional
    LCCN
    2007-000951
    Dewey Edition
    22
    Grade From
    College Graduate Student
    Illustrated
    Yes
    Dewey Decimal
    006.3/1
    Synopsis
    Advanced statistical modeling and knowledge representation techniques for a newly emerging area of machine learning and probabilistic reasoning; includes introductory material, tutorials for different proposed approaches, and applications., Advanced statistical modeling and knowledge representation techniques for a newly emerging area of machine learning and probabilistic reasoning; includes introductory material, tutorials for different proposed approaches, and applications. Handling inherent uncertainty and exploiting compositional structure are fundamental to understanding and designing large-scale systems. Statistical relational learning builds on ideas from probability theory and statistics to address uncertainty while incorporating tools from logic, databases and programming languages to represent structure. In Introduction to Statistical Relational Learning , leading researchers in this emerging area of machine learning describe current formalisms, models, and algorithms that enable effective and robust reasoning about richly structured systems and data. The early chapters provide tutorials for material used in later chapters, offering introductions to representation, inference and learning in graphical models, and logic. The book then describes object-oriented approaches, including probabilistic relational models, relational Markov networks, and probabilistic entity-relationship models as well as logic-based formalisms including Bayesian logic programs, Markov logic, and stochastic logic programs. Later chapters discuss such topics as probabilistic models with unknown objects, relational dependency networks, reinforcement learning in relational domains, and information extraction. By presenting a variety of approaches, the book highlights commonalities and clarifies important differences among proposed approaches and, along the way, identifies important representational and algorithmic issues. Numerous applications are provided throughout.
    LC Classification Number
    QA76.9.D3I68 2007

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