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    • Stellar Spectral Classification with Minimum Within-Class and Maximum Between-Class Scatter Support Vector Machine

      Liu Zhong-Bao

      Volume 37 Issue 2 June 2016 Article ID 0000009

    • Fulltext

       

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      Permanent link:
      https://www.ias.ac.in/article/fulltext/joaa/037/02/0000009

    • Keywords

       

      Stellar spectral classification; support vector machine (SVM); Fisher’s discriminant analysis (FDA); class distribution.

    • Abstract

       

      Support Vector Machine (SVM) is one of the important stellar spectral classification methods, and it is widely used in practice. But its classification efficiencies cannot be greatly improved because it does not take the class distribution into consideration. In view of this, a modified SVM-named Minimum within-class and Maximum between-class scatter Support Vector Machine (MMSVM) is constructed to deal with the above problem. MMSVM merges the advantages of Fisher’s Discriminant Analysis (FDA) and SVM, and the comparative experiments on the Sloan Digital Sky Survey (SDSS) show that MMSVM performs better than SVM.

    • Author Affiliations

       

      Liu Zhong-Bao1

      1. School of Computer and Control Engineering, North University of China, Taiyuan 030051, China.

    • Dates

       
      Published
      1 June 2016
      Manuscript received
      12 November 2015
      Accepted
      29 December 2015

  • Journal of Astrophysics and Astronomy | News

    • Continuous Article Publication

      Posted on January 27, 2016

      Since January 2016, the Journal of Astrophysics and Astronomy has moved to Continuous Article Publishing (CAP) mode. This means that each accepted article is being published immediately online with DOI and article citation ID with starting page number 1. Articles are also visible in Web of Science immediately. All these have helped shorten the publication time and have improved the visibility of the articles.

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