Deep Learning Algorithm Based Support Vector Machines

被引：1

作者：

Naji, Mohamad ^{[1
]}

Alyassine, Widad ^{[1
]}

Nizamani, Qurat Ul Ain ^{[2
]}

Zhang, Lingrui ^{[1
]}

Wei, Xue ^{[1
]}

Xu, Ziqiu ^{[1
]}

Braytee, Ali ^{[1
]}

Anaissi, Ali ^{[1
]}

机构：

[1] Univ Sydney, Sch Comp Sci, Sydney, NSW, Australia

[2] Kent Inst Australia, Sch Comp Sci, Sydney, NSW, Australia

来源：

PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON INNOVATIONS IN COMPUTING RESEARCH (ICR'22) | 2022年 / 1431卷

关键词：

Deep learning; Support vector machine; Convolution neural network; CLASSIFIER; SVM;

D O I：

10.1007/978-3-031-14054-9_27

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper proposes a new deep learning model which replaces the softmax activation function with support vector machines. To evaluate the performance of the model, we have completed a total of four sets of codes, including the traditional svm classification model, the traditional cnn model, the model of svm behind the fully connected layer, and the model of svm instead of softmax. In order to compare the accuracy of these four groups of models, we trained and tested three data sets, namely the mnist data set, the CIFAR-10 data set and the compass x-ray data set.

引用

页码：281 / 289

页数：9

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