Kernels for structured data

書誌事項

Kernels for structured data

Thomas Gärtner

(Series in machine perception and artificial intelligence / editors, H. Bunke, P.S.P. Wang, v. 72)

World Scientific, c2008

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注記

Includes bibliographical references (p. 179-190) and index

内容説明・目次

内容説明

This book provides a unique treatment of an important area of machine learning and answers the question of how kernel methods can be applied to structured data. Kernel methods are a class of state-of-the-art learning algorithms that exhibit excellent learning results in several application domains. Originally, kernel methods were developed with data in mind that can easily be embedded in a Euclidean vector space. Much real-world data does not have this property but is inherently structured. An example of such data, often consulted in the book, is the (2D) graph structure of molecules formed by their atoms and bonds. The book guides the reader from the basics of kernel methods to advanced algorithms and kernel design for structured data. It is thus useful for readers who seek an entry point into the field as well as experienced researchers.

目次

  • Why Kernels for Structured Data?
  • Kernel Methods in a Nutshell
  • Kernell Design
  • Basic Term Kernels
  • Graph Kernels.

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詳細情報

  • NII書誌ID(NCID)
    BA87765372
  • ISBN
    • 9789812814555
  • 出版国コード
    us
  • タイトル言語コード
    eng
  • 本文言語コード
    eng
  • 出版地
    New Jersey
  • ページ数/冊数
    xvii, 197 p.
  • 大きさ
    24 cm
  • 分類
  • 件名
  • 親書誌ID
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