Introduction to Statistical Relational Learning (Hardcover)

Lise Getoor, Ben Taskar

  • 出版商: MIT
  • 出版日期: 2007-11-01
  • 售價: $2,400
  • 貴賓價: 9.5$2,280
  • 語言: 英文
  • 頁數: 608
  • 裝訂: Hardcover
  • ISBN: 0262072882
  • ISBN-13: 9780262072885
  • 相關分類: SQL
  • 海外代購書籍(需單獨結帳)

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商品描述

Description

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.

商品描述(中文翻譯)

描述

處理固有的不確定性並利用組合結構是理解和設計大規模系統的基礎。統計關聯學習建立在概率論和統計學的思想基礎上,以解決不確定性問題,同時結合邏輯、數據庫和編程語言的工具來表示結構。在《統計關聯學習入門》這本書中,這一新興的機器學習領域的領先研究人員描述了當前的形式化方法、模型和算法,以實現對豐富結構系統和數據的有效和穩健的推理。

早期章節提供了後續章節中使用的教程,介紹了圖形模型中的表示、推理和學習,以及邏輯的概念。然後,書中描述了面向對象的方法,包括概率關聯模型、關聯馬爾可夫網絡和概率實體關係模型,以及基於邏輯的形式化方法,包括貝葉斯邏輯程序、馬爾可夫邏輯和隨機邏輯程序。後面的章節討論了一些主題,如具有未知對象的概率模型、關聯依賴網絡、關聯領域中的強化學習和信息提取。

通過介紹多種方法,本書突出了提出的方法之間的共同點,並澄清了重要的差異,同時還確定了重要的表示和算法問題。全書提供了大量的應用案例。