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Kernel Parameter Optimization for Support Vector Machine Based on Sliding Mode Control

Support Vector Machine (SVM) is a supervised machine learning algorithm, which is used for robust and accurate classification. Despite its advantages, its classification speed deteriorates due to its large number of support vectors when dealing with large scale problems and dependency of its perform...

Ausführliche Beschreibung

Gespeichert in:
Hauptverfasser: Maryam Yalsavar, Paknoosh Karimaghaee, Akbar Sheikh-Akbari, Mohammad-Hassan Khooban, Jamshid Dehmeshki, Salah Al-Majeed
Format: Artigo
Sprache:Inglês
Veröffentlicht: IEEE 2022-01-01
Schriftenreihe:IEEE Access
Schlagworte:
Support vector machine
sliding mode control
RBF kernel
2-degree polynomial kernel
optimal parameter
classification speed
Online-Zugang:https://ieeexplore.ieee.org/document/9707762/
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