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あぁかがさざただなはばぱまやゃらわゎんいぃきぎしじちぢにひびぴみりうぅくぐすずつづっぬふぶぷむゆゅるえぇけげせぜてでねへべぺめれおぉこごそぞとどのほぼぽもよょろを

アァカサザタダナハバパマヤャラワヮンイィキギシジチヂニヒビピミリウゥクグスズツヅッヌフブプムユュルエェケゲセゼテデヘベペメレオォコゴソゾトドノホボポモヨョロヲ ―

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1
A new multi-class classification method based on minimum enclosing balls

QingJun Song,XingMing Xiao,HaiYan Jiang,XieGuang Zhao 대한기계학회 2015 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.29 No.8
With respect to classification problems, the Minimum enclosing ball (MEB) method was recently studied by some scholars as a newsupport vector machine. As a nascent technology, however, MEB reports poor adaptability for different types of samples, especiallymulti-class samples. In this paper, we propose a new multi-class classification method based on MEB. This method is derived from eachclass sample center and radius with the Gaussian kernel width factor parameter σ, which is labelled as σ-MEB. σ is a variable parameteraccording to the different sample characteristics. When this parameter is considered, the multi-class classifier is easy to adapt and is robustin diverse datasets. The quadratic programming problem was transformed into its dual form with Lagrange multipliers using thismethod. Finally, we applied sequential minimal optimization method and Karush—Kuhn—Tucker conditions to accelerate the trainingprocess. Numerical experiment results indicate that given different types of samples, the proposed method is more accurate than themethods with which it is compared. Moreover, the proposed method reports values in the upper quantile with respect to adaptation performance.