Selected article for: "support SVM vector machine and vector machine"
Author: Saban Ozturk; Umut Ozkaya; Mucahid Barstugan
Title: Classification of Coronavirus Images using Shrunken Features
  • Document date: 2020_4_6
    • ID: 2l1zw19o_14
    Snippet: For this reason, the size of the feature vector is reduced by using a stacked auto-encoder (sAE) and principal component analysis (PCA). The success of these two feature reduction methods, which work according to different approaches, is compared. Thus, both storage space is saved, and response time is accelerated. Finally, data is classified with the support vector machine (SVM). This paper is organized as follows. Section 2 describes details ab.....
    Document: For this reason, the size of the feature vector is reduced by using a stacked auto-encoder (sAE) and principal component analysis (PCA). The success of these two feature reduction methods, which work according to different approaches, is compared. Thus, both storage space is saved, and response time is accelerated. Finally, data is classified with the support vector machine (SVM). This paper is organized as follows. Section 2 describes details about the dataset, the introduction of the proposed framework method, and parameters. Section 3 presents experiments and experimental results. Section 4 includes discussion, and Section 5 presents the conclusion.

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