Support Vector Machines: Optimization Based Theory, Algorithms, and Extensions (Hardcover)

Naiyang Deng, Yingjie Tian, Chunhua Zhang

  • 出版商: CRC
  • 出版日期: 2012-12-17
  • 售價: $3,600
  • 貴賓價: 9.5$3,420
  • 語言: 英文
  • 頁數: 363
  • 裝訂: Hardcover
  • ISBN: 143985792X
  • ISBN-13: 9781439857922
  • 相關分類: Algorithms-data-structures
  • 立即出貨 (庫存=1)

買這商品的人也買了...

商品描述

Support Vector Machines: Optimization Based Theory, Algorithms, and Extensions presents an accessible treatment of the two main components of support vector machines (SVMs)—classification problems and regression problems. The book emphasizes the close connection between optimization theory and SVMs since optimization is one of the pillars on which SVMs are built.

 

The authors share insight on many of their research achievements. They give a precise interpretation of statistical leaning theory for C-support vector classification. They also discuss regularized twin SVMs for binary classification problems, SVMs for solving multi-classification problems based on ordinal regression, SVMs for semi-supervised problems, and SVMs for problems with perturbations.

 

To improve readability, concepts, methods, and results are introduced graphically and with clear explanations. For important concepts and algorithms, such as the Crammer-Singer SVM for multi-class classification problems, the text provides geometric interpretations that are not depicted in current literature.

 

Enabling a sound understanding of SVMs, this book gives beginners as well as more experienced researchers and engineers the tools to solve real-world problems using SVMs.

商品描述(中文翻譯)

《支持向量機:基於優化的理論、算法和擴展》提供了對支持向量機(SVM)的兩個主要組成部分——分類問題和回歸問題的易於理解的介紹。該書強調了優化理論與SVM之間的密切聯繫,因為優化是SVM建立在其基礎上的支柱之一。

作者分享了他們的許多研究成果。他們對C支持向量分類的統計學習理論進行了精確的解釋。他們還討論了用於二元分類問題的正則化雙支持向量機、基於序回歸的多分類問題的支持向量機、半監督問題的支持向量機以及帶有擾動的問題的支持向量機。

為了提高可讀性,概念、方法和結果以圖形和清晰的解釋方式介紹。對於重要的概念和算法,例如用於多分類問題的Crammer-Singer SVM,本書提供了在當前文獻中未描述的幾何解釋。

通過使讀者對SVM有一個牢固的理解,這本書為初學者以及更有經驗的研究人員和工程師提供了使用SVM解決現實世界問題的工具。