Predicting Structured Data (Hardcover)

Gökhan H. Bakir, Thomas Hofmann, Bernhard Schölkopf, Alexander J. Smola

  • 出版商: MIT
  • 出版日期: 2007-07-27
  • 售價: $1,500
  • 貴賓價: 9.8$1,470
  • 語言: 英文
  • 頁數: 360
  • 裝訂: Hardcover
  • ISBN: 0262026171
  • ISBN-13: 9780262026178
  • 相關分類: 大數據 Big-dataMachine LearningData Science
  • 立即出貨

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Description

Machine learning develops intelligent computer systems that are able to generalize from previously seen examples. A new domain of machine learning, in which the prediction must satisfy the additional constraints found in structured data, poses one of machine learning’s greatest challenges: learning functional dependencies between arbitrary input and output domains. This volume presents and analyzes the state of the art in machine learning algorithms and theory in this novel field. The contributors discuss applications as diverse as machine translation, document markup, computational biology, and information extraction, among others, providing a timely overview of an exciting field.

商品描述(中文翻譯)

描述

機器學習開發能夠從先前觀察到的例子中進行泛化的智能計算機系統。機器學習的一個新領域,其中預測必須滿足結構化數據中的附加約束,提出了機器學習最大的挑戰之一:學習任意輸入和輸出域之間的功能依賴關係。本書介紹並分析了這一新領域中機器學習算法和理論的最新狀態。貢獻者們討論了各種應用,包括機器翻譯、文檔標記、計算生物學和信息提取等,提供了對這一令人興奮的領域的及時概述。