Extraction of relevant dataset for support vector machine training: A comparison

机译：支持向量机训练相关数据集的提取：比较

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Support Vector Machine (SVM) is a popular machine learning technique for classification. SVM is computationally infeasible with large dataset due to its large training time. In this paper we compare three different methods for training time reduction of SVM. Different combination of Decision Tree (DT), Fisher Linear Discriminant (FLD), QR Decomposition (QRD) and Modified Fisher Linear Discriminant (MFLD) makes reduced dataset for SVM training. Experimental results indicates that SVM with QRD and MFLD have good classification accuracy with significantly smaller training time.

机译：支持向量机（SVM）是一个用于分类的流行的机器学习技术。由于其大型培训时间，SVM通过大型数据集进行计算地不可行。在本文中，我们比较三种不同的方法来训练时间减少SVM。决策树（DT），Fisher线性判别（FLD），QR分解（QRD）和改进的Fisher线性判别（MFLD）的不同组合使得SVM培训减少了DataSet。实验结果表明，具有QRD和MFLD的SVM具有良好的分类准确性，培训时间明显更小。

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《International conference on advances in computing, communications and informatics》|2015年||共6页
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Adeena K D; Remya R;
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正文语种
中图分类通信;
关键词
Decision Tree; Fisher Linear Discriminant; Modified Fisher Linear Discriminant; QR decomposition; SVM;

机译：决策树;Fisher线性判别;改性Fisher线性判别;QR分解;SVM;

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Extraction of relevant dataset for support vector machine training: A comparison

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