Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning) 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024

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Getoor, Lise 作者

The MIT Press

譯者

2007-11-30 出版日期

608 頁數

USD 53.00 價格

Hardcover

Adaptive Computation and Machine Learning 叢書系列

9780262072885 圖書編碼

Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning) 在線電子書圖書標籤: 機器學習統計學習計算機統計計算機科學經典模式分類概率論與統計學

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Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning) 在線電子書 epub 下載 mobi 下載 pdf 下載 txt 下載 2024

Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning) 在線電子書 epub 下載 pdf 下載 mobi 下載 txt 下載 2024

Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning) 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024

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各種關係學習模型的介紹。當然，論文集形式的書多少會有些淩亂。

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各種關係學習模型的介紹。當然，論文集形式的書多少會有些淩亂。

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各種關係學習模型的介紹。當然，論文集形式的書多少會有些淩亂。

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brief knowledge of most of hot spots

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各種關係學習模型的介紹。當然，論文集形式的書多少會有些淩亂。

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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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