Data Classification: Algorithms and Applications (Hardcover)

Charu C. Aggarwal

  • 出版商: CRC
  • 出版日期: 2014-07-25
  • 售價: $3,500
  • 貴賓價: 9.5$3,325
  • 語言: 英文
  • 頁數: 707
  • 裝訂: Hardcover
  • ISBN: 1466586745
  • ISBN-13: 9781466586741
  • 相關分類: Algorithms-data-structures
  • 立即出貨 (庫存 < 4)

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商品描述

Comprehensive Coverage of the Entire Area of Classification

Research on the problem of classification tends to be fragmented across such areas as pattern recognition, database, data mining, and machine learning. Addressing the work of these different communities in a unified way, Data Classification: Algorithms and Applications explores the underlying algorithms of classification as well as applications of classification in a variety of problem domains, including text, multimedia, social network, and biological data.

This comprehensive book focuses on three primary aspects of data classification:

  • Methods-The book first describes common techniques used for classification, including probabilistic methods, decision trees, rule-based methods, instance-based methods, support vector machine methods, and neural networks.
  • Domains-The book then examines specific methods used for data domains such as multimedia, text, time-series, network, discrete sequence, and uncertain data. It also covers large data sets and data streams due to the recent importance of the big data paradigm.
  • Variations-The book concludes with insight on variations of the classification process. It discusses ensembles, rare-class learning, distance function learning, active learning, visual learning, transfer learning, and semi-supervised learning as well as evaluation aspects of classifiers.

商品描述(中文翻譯)

《資料分類:演算法與應用》是一本全面涵蓋分類領域的書籍。研究分類問題通常分散在模式識別、資料庫、資料探勘和機器學習等領域。本書以統一的方式探討這些不同社群的工作,介紹了分類的基本演算法以及在文本、多媒體、社交網絡和生物資料等各種問題領域中的應用。

本書主要關注資料分類的三個主要方面:
- 方法:首先介紹了常用的分類技術,包括概率方法、決策樹、基於規則的方法、基於實例的方法、支持向量機方法和神經網絡。
- 領域:然後探討了在多媒體、文本、時間序列、網絡、離散序列和不確定資料等特定領域中使用的方法。同時也涵蓋了大數據和資料流的處理,因為這些在當今大數據時代非常重要。
- 變體:最後討論了分類過程的變體。包括集成學習、稀有類別學習、距離函數學習、主動學習、視覺學習、遷移學習和半監督學習,以及分類器的評估方面。

這本書提供了對分類領域的全面覆蓋,並且對於從事相關研究或應用的讀者來說是一個寶貴的資源。