tianrui-qi/ADMM-for-SVM
Alternating Direction Method of Multipliers for Support Vector Machine
This project helps data scientists efficiently build a Support Vector Machine (SVM) model for classifying data into two categories. It takes in labeled datasets where each data point belongs to one of two classes and outputs the optimal hyperplane and its parameters that best separates these classes. The ideal user is a data scientist or machine learning engineer who needs to develop robust classification models.
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Use this if you need to find the best linear boundary to separate two distinct groups within your data, such as in medical diagnosis, image recognition, or sentiment analysis.
Not ideal if your data cannot be separated by a straight line, as this project focuses on linear SVMs, or if you require multi-class classification.
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10
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Language
MATLAB
License
MIT
Category
Last pushed
Aug 28, 2025
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