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FUZZY SVM BASED ON TRIANGULAR FUZZY NUMBERS

机译:基于三角模糊数的模糊SVM

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摘要

Support vector machine (SVM) is novel type learning machine, based on statistical learning theory, whose tasks involve classification, regression or novelty detection.Traditional SVM classifies the data with numerical features.However, in most cases of real world, there are much more data with fuzzy features.It is difficult to apply traditional SVM to fuzzy data directly to classify.In this paper, we provide a fuzzy SVM for the data with triangular fuzzy number features.The designing fundamentals and method of computation and realization are given.The experiment results show that the new method proposed in this paper is more effective and practical.This new method optimizes the classified result of support vector machine and enhances the intelligent level of support vector machine.
机译:支持向量机(SVM)是一种基于统计学习理论的新型学习机,其任务涉及分类,回归或新颖性检测。传统的SVM对数据进行数值分类,但是在现实世界中大多数情况下,支持向量机(SVM)的数量更多。具有模糊特征的数据,很难将传统的支持向量机直接应用于模糊数据进行分类。本文针对具有三角模糊数特征的数据提供了模糊支持向量机,给出了设计的基本原理和计算及实现方法。实验结果表明,本文提出的新方法更加有效和实用。该方法优化了支持向量机的分类结果,提高了支持向量机的智能水平。

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