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Title:Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning) (Adaptive Computation and Machine Learning Series)
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Publisher:MIT Press
ISBN:0262072882
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Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning) (Adaptive Computation and Machine Learning Series) by Lise Getoor, Ben Taskar

PDF, EPUB, MOBI, TXT, DOC Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning) (Adaptive Computation and Machine Learning Series) 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 Lise Getoor is Assistant Professor in the Department of Computer Science at the University of Maryland Ben Taskar is Assistant Professor in the Computer and Information Science Department at the University of Pennsylvania

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    This book Home of eBook - Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning) (Adaptive Computation and Machine Learning Series) - Handling inherent uncertainty and exploiting compositional structure are fundamental to understanding and designing large scale systems Statistical relational l...

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  • Privacy in Social Networks

    This book Home of eBook - Privacy in Social Networks - This synthesis lecture provides a survey of work on privacy in online social networks OSNs This work encompasses concerns of users as well as service providers ...

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Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning) (Adaptive Computation and Machine Learning Series), Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning series), Privacy in Social Networks
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 Lise Getoor is Assistant Professor in the Department of Computer Science at the University of Maryland Ben Taskar is Assistant Professor in the Computer and Information Science Department at the University of Pennsylvania, This synthesis lecture provides a survey of work on privacy in online social networks OSNs This work encompasses concerns of users as well as service providers and third parties Our goal is to approach such concerns from a computer science perspective and building upon existing work on privacy security statistical modeling and databases to provide an overview of the technical and algorithmic issues related to privacy in OSNs We start our survey by introducing a simple OSN data model and describe common statistical inference techniques that can be used to infer potentially sensitive information Next we describe some privacy definitions and privacy mechanisms for data publishing Finally we describe a set of recent techniques for modeling evaluating and managing individual users privacy risk within the context of OSNs Table of Contents Introduction A Model for Online Social Networks Types of Privacy Disclosure Statistical Methods for Inferring Information in Networks Anonymity and Differential Privacy Attacks and Privacy preserving Mechanisms Models of Information Sharing Users Privacy Risk Management of Privacy Settings, No description available