國立虎尾科技大學 |

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

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Comparison of Kernel Functions and Parameter Selection of SVM Classification Algorithms./
作者:	Pan, Linying.
面頁冊數:	1 online resource (53 pages)
附註:	Source: Masters Abstracts International, Volume: 85-06.
Contained By:	Masters Abstracts International85-06.
標題:	Computer science. -
電子資源:	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:

573171
Computer science.
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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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.
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653 $a Generalization performance
653 $a Kernel function
653 $a Support Vector Machine
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