Predicting Structured Data
暫譯: 預測結構化數據

Bakir, Gokhan, Hofmann, Thomas, Scholkopf, Bernhard

Predicting Structured Data
  • 出版商: Summit Valley Press
  • 出版日期: 2007-07-27
  • 售價: $2,140
  • 貴賓價: 9.5 折 $2,033
  • 語言: 英文
  • 頁數: 362
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 0262528045
  • ISBN-13: 9780262528047

    • 海外代購書籍(需單獨結帳)

商品描述

State-of-the-art algorithms and theory in a novel domain of machine learning, prediction when the output has structure.

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.

Contributors
Yasemin Altun, G khan Bakir, Olivier Bousquet, Sumit Chopra, Corinna Cortes, Hal Daum III, Ofer Dekel, Zoubin Ghahramani, Raia Hadsell, Thomas Hofmann, Fu Jie Huang, Yann LeCun, Tobias Mann, Daniel Marcu, David McAllester, Mehryar Mohri, William Stafford Noble, Fernando P rez-Cruz, Massimiliano Pontil, Marc'Aurelio Ranzato, Juho Rousu, Craig Saunders, Bernhard Sch lkopf, Matthias W. Seeger, Shai Shalev-Shwartz, John Shawe-Taylor, Yoram Singer, Alexander J. Smola, Sandor Szedmak, Ben Taskar, Ioannis Tsochantaridis, S.V.N Vishwanathan, Jason Weston

商品描述(中文翻譯)

在機器學習新領域中的尖端演算法與理論，當輸出具有結構時的預測。

機器學習發展出能夠從先前見過的範例中進行概括的智能計算機系統。機器學習的一個新領域中，預測必須滿足結構化數據中的額外約束，這對機器學習提出了最大的挑戰之一：學習任意輸入和輸出領域之間的功能依賴性。本書呈現並分析了在這一新領域中機器學習演算法和理論的最新進展。貢獻者討論了機器翻譯、文件標記、計算生物學和信息提取等多樣化的應用，提供了這一令人興奮的領域的及時概述。

貢獻者
Yasemin Altun, G khan Bakir, Olivier Bousquet, Sumit Chopra, Corinna Cortes, Hal Daum III, Ofer Dekel, Zoubin Ghahramani, Raia Hadsell, Thomas Hofmann, Fu Jie Huang, Yann LeCun, Tobias Mann, Daniel Marcu, David McAllester, Mehryar Mohri, William Stafford Noble, Fernando P rez-Cruz, Massimiliano Pontil, Marc'Aurelio Ranzato, Juho Rousu, Craig Saunders, Bernhard Sch lkopf, Matthias W. Seeger, Shai Shalev-Shwartz, John Shawe-Taylor, Yoram Singer, Alexander J. Smola, Sandor Szedmak, Ben Taskar, Ioannis Tsochantaridis, S.V.N Vishwanathan, Jason Weston

作者簡介

S. V. N. Vishwanathan is an Assistant Professor of Statistics and Computer Science at Purdue University and Senior Researcher in the Statistical Machine Learning Program, National ICT Australia with an adjunct appointment at the Research School for Information Sciences and Engineering, Australian National University.

作者簡介(中文翻譯)

S. V. N. Vishwanathan 是普渡大學統計學與計算機科學的助理教授，並且是澳大利亞國家資訊與通信技術研究所統計機器學習計畫的高級研究員，同時在澳大利亞國立大學資訊科學與工程研究學院擔任兼任職位。

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