Support Vector Machines and Perceptrons: Learning, Optimization, Classification, and Application to Social Networks (SpringerBriefs in Computer Science)

M.N. Murty, Rashmi Raghava

  • 出版商: Springer
  • 出版日期: 2016-08-25
  • 售價: $2,450
  • 貴賓價: 9.5$2,328
  • 語言: 英文
  • 頁數: 95
  • 裝訂: Paperback
  • ISBN: 3319410628
  • ISBN-13: 9783319410623
  • 相關分類: Algorithms-data-structuresComputer-Science
  • 立即出貨 (庫存 < 3)

商品描述

This work reviews the state of the art in SVM and perceptron classifiers. A Support Vector Machine (SVM) is easily the most popular tool for dealing with a variety of machine-learning tasks, including classification. SVMs are associated with maximizing the margin between two classes. The concerned optimization problem is a convex optimization guaranteeing a globally optimal solution. The weight vector associated with SVM is obtained by a linear combination of some of the boundary and noisy vectors. Further, when the data are not linearly separable, tuning the coefficient of the regularization term becomes crucial. Even though SVMs have popularized the kernel trick, in most of the practical applications that are high-dimensional, linear SVMs are popularly used. The text examines applications to social and information networks. The work also discusses another popular linear classifier, the perceptron, and compares its performance with that of the SVM in different application areas.>

商品描述(中文翻譯)

這份工作回顧了SVM和感知器分類器的最新技術。支持向量機(SVM)是處理各種機器學習任務(包括分類)最受歡迎的工具。SVM與最大化兩個類別之間的邊界有關。相關的優化問題是一個凸優化問題,保證了全局最優解。與SVM相關的權重向量是由一些邊界和噪聲向量的線性組合獲得的。此外,當數據不是線性可分時,調整正則化項的係數變得至關重要。儘管SVM已經普及了核技巧,但在大多數高維實際應用中,線性SVM被廣泛使用。該文本還探討了另一種流行的線性分類器——感知器,並將其在不同應用領域中的性能與SVM進行了比較。