Time Series Data Pattern Classification using Fuzzy Membership Functions and Support Vector Machines--KOSPI 200: Korea Composite Stock Price Index 200 (特集 「アクティブマイニング」及び一般)

スポンサーリンク

概要

• 論文の詳細を見る

• SVM (Support Vector Machine) is a binary classifier proposed by Vapnik. SVM has proven performance in various application fields by minimizing misclassification based on mathematical theories. Recently, FSVM that applies Fuzzy membership function to SVM has been proposed. In this study, it is proven that FSVM (polynomial kernel) has reduced learning time better than SVM when fuzzy membership functions of FSVM have been expanded from 2-dimension to bigger than 3 dimension.

• 2003-09-14

著者

• Lee S

  Sogang Univ. Seoul Kor

• Paik Euree

  Dept. Of Economics London School Of Economics And Political Science

• Lee Sooyong

  Dept. Of Computer Science Yonsei University

• LEE Yillbyng

  Division of and Information Engineering, and of BERC, Yonsei University

• Lee Yillbyng

  Dept. of Economics, London School of Economics and Political Science

• Lee Yillbyng

  Division Of And Information Engineering And Of Berc Yonsei University

• Paik Euree

  Division of and Information Engineering, and of BERC, Yonsei University

関連論文

• Time Series Data Pattern Classification using Fuzzy Membership Functions and Support Vector Machines--KOSPI 200: Korea Composite Stock Price Index 200 (特集 「アクティブマイニング」及び一般)
• (「アクティブマイニング」及び一般)
• Time Series Data Pattern Classification using Fuzzy Membership Functions and Support Vector Machines : KOSPI 200 : Korea Composite Stock Price Index 200
• Time Series Data Pattern Classification using Fuzzy Membership Functions and Support Vector Machines--KOSPI 200:Korea Composite Stock Price Index 200 (小特集 「アクティブマイニング」および一般)

スポンサーリンク