rarely used spare parts

According to this, the thesis does some research on support vector machines(SVMs)based forecasting method for rarely used spare parts demand.
基於此,本文研究了基於支持曏量機(jī)的不常用備件需求預(yù)測方法。

計(jì)算機(jī)科學(xué)技術(shù)

To address this problem above, the thesis makes efforts on application of multiple support vector machines (SVM) to forecast rarely used spare parts demand via designing an integrated forecasting support system.
本文引入基於有限樣本統(tǒng)計(jì)學(xué)習(xí)理論的支持曏量機(jī)廻歸方法,預(yù)測不常用備件需求。