Linear Classifier Implementation in MATLAB

This article introduces the implementation of linear classifiers using MATLAB. A linear classifier represents a fundamental machine learning model suitable for categorizing data points into two or more distinct classes. We provide a comprehensive code package containing visualization charts, MATLAB .m files, and detailed experimental reports to demonstrate how to create and train linear classifiers in MATLAB. The implementation covers key algorithms such as perceptron learning and linear discriminant analysis, with code examples showing weight initialization, decision boundary calculation, and classification accuracy evaluation. Furthermore, we discuss various linear classifier algorithms and provide guidance on selecting the optimal algorithm based on dataset characteristics. Through this tutorial, you will learn to implement basic machine learning models using MATLAB, establishing a solid foundation for future machine learning projects. The code includes functions for data preprocessing, model training with gradient descent optimization, and performance visualization using confusion matrices and ROC curves.