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Comparison of Kernel Functions and Parameter Selection of SVM Classification Algorithms.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Comparison of Kernel Functions and Parameter Selection of SVM Classification Algorithms./
Author:	Pan, Linying.
Description:	1 online resource (53 pages)
Notes:	Source: Masters Abstracts International, Volume: 85-06.
Contained By:	Masters Abstracts International85-06.
Subject:	Statistics. -
Online resource:	click for full text (PQDT)
ISBN:	9798381112320

Comparison of Kernel Functions and Parameter Selection of SVM Classification Algorithms.
Pan, Linying.

Comparison of Kernel Functions and Parameter Selection of SVM Classification Algorithms. - 1 online resource (53 pages)

Source: Masters Abstracts International, Volume: 85-06.

Thesis (M.S.)--University of California, Los Angeles, 2023.

Includes bibliographical references

Support Vector Machine (SVM) is a reliable supervised learning model extensively utilized for classification and regression tasks, owing to its remarkable ability to achieve strong generalization performance. This study focuses on two key factors in the SVM model: the error penalty parameter C and the kernel function. The C parameter is used to balance the model's complexity and empirical risk, and its selection is crucial for SVM performance. A smaller C value may lead to underfitting, while a larger C can result in overfitting. Additionally, the choice of the kernel function also significantly impacts SVM performance. We will investigate the effects of different kernel functions and parameter settings in the classification task of the Iris dataset and visualize their impacts through a visual approach. The study's results indicate that, in most cases, the Gaussian kernel outperforms other kernel functions, exhibiting superior classification performance and generalization capability. Therefore, we opt for the Gaussian Radial Basis Function (RBF) kernel and conduct experiments to evaluate the influence of different parameter configurations on classification performance.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798381112320Subjects--Topical Terms:

556824
Statistics.
Subjects--Index Terms:

ClassificationIndex Terms--Genre/Form:

554714
Electronic books.

Comparison of Kernel Functions and Parameter Selection of SVM Classification Algorithms.
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245 1 0 $a Comparison of Kernel Functions and Parameter Selection of SVM Classification Algorithms.
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500 $a Source: Masters Abstracts International, Volume: 85-06.
500 $a Advisor: Wu, Yingnian.
502 $a Thesis (M.S.)--University of California, Los Angeles, 2023.
504 $a Includes bibliographical references
520 $a Support Vector Machine (SVM) is a reliable supervised learning model extensively utilized for classification and regression tasks, owing to its remarkable ability to achieve strong generalization performance. This study focuses on two key factors in the SVM model: the error penalty parameter C and the kernel function. The C parameter is used to balance the model's complexity and empirical risk, and its selection is crucial for SVM performance. A smaller C value may lead to underfitting, while a larger C can result in overfitting. Additionally, the choice of the kernel function also significantly impacts SVM performance. We will investigate the effects of different kernel functions and parameter settings in the classification task of the Iris dataset and visualize their impacts through a visual approach. The study's results indicate that, in most cases, the Gaussian kernel outperforms other kernel functions, exhibiting superior classification performance and generalization capability. Therefore, we opt for the Gaussian Radial Basis Function (RBF) kernel and conduct experiments to evaluate the influence of different parameter configurations on classification performance.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
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650 4 $a Computer engineering. $3 569006
650 4 $a Computer science. $3 573171
653 $a Classification
653 $a Error penalty parameter
653 $a Generalization performance
653 $a Kernel function
653 $a Support Vector Machine
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