作者: Zhiquan Qi , Vassil Alexandrov , Yong Shi , Yingjie Tian , None
DOI: 10.1016/J.PROCS.2014.05.245
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摘要: In this paper, we proposed a new parallel algorithm: Parallel Regularized Multiple-Criteria Linear Programming (PRMCLP) to overcome the computing and storage requirements increased rapidly with number of training samples. Firstly, convert RMCLP model into unconstrained optimization problem, then split it several parts, each part is computed by single processor. After that, analyze part's result for next cycle going. By doing this, are be able obtain final solution whole classification problem. All experiments in public datasets show that our method greatly increases speed help multiple processors.