Kernel Functions for the Support Vector Machine: Comparing Performances on Crude Oil Price Data
作者: Haruna ChiromaSameem AbdulkareemAdamu I. AbubakarTutut Herawan
作者单位: Department of Artificial Intelligence, University of Malaya;;Department of Information systems, University of Malaya;;Department of Information system, International Islamic University;;AMCS Research Center
英文丛书称: Advances in Intelligent Systems and Computing
出版社: Springer International Publishing,   2014
ISBN: 978-3-319-07691-1
来源数据库: Springer Nature Book
DOI: 10.1007/978-3-319-07692-8_26
关键词: Radial basis functionPolynomialExponentialSigmoidWaveCrude oil price
原始语种摘要: Abstract The purpose of this research is to broaden the theoretic understanding of the effects of kernel functions for the support vector machine on crude oil price data. The performances of five (5) kernel functions of the support vector machine were compared. The analysis of variance was used for validating the results and we take additional steps to study the Post Hoc. Findings emanated from the research indicated that the performance of the wave kernel function was statistically significantly better than the radial basis function, polynomial, exponential, and sigmoid kernel functions. Computational efficiency of the wave activation function was poor compared with the other kernel functions in the study. This research could provide a better understanding of the behavior of the kernel...
全文获取路径: Springer Nature 

  • kernel 
  • Data 数据
  • Vector 矢量
  • machine 机器
  • sigmoid s状的
  • exponential 指数的
  • crude 生的
  • price 价格
  • support 支柱
  • understanding 理解