Application of support vector machines for categorizing biological and medical data

Zhanyang Jin; Tejada Gurmendi Hei; Kewei Jin

doi:10.1117/12.3026871

27 March 2024 Application of support vector machines for categorizing biological and medical data

Zhanyang Jin, Tejada Gurmendi Hei, Kewei Jin

Proceedings Volume 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023); 131054D (2024) https://doi.org/10.1117/12.3026871
Event: 3rd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 2023, Qingdao, China

Abstract

Support Vector Machines (SVM) have gained significant attention in the field of machine learning due to their robust performance in various classification tasks. In this paper, we explore the application of SVM to categorize Iris flowers into three distinct types based on their features. SVM is a supervised learning algorithm that finds a hyperplane in a high-dimensional feature space to separate classes while maximizing the margin between them. The purpose of this study is to demonstrate the effectiveness of SVM in accurately classifying Iris flowers and to showcase its significance in real-world classification problems.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhanyang Jin, Tejada Gurmendi Hei, and Kewei Jin "Application of support vector machines for categorizing biological and medical data", Proc. SPIE 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 131054D (27 March 2024); https://doi.org/10.1117/12.3026871

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available