Linear Classification Using Perceptron Algorithm with MATLAB Implementation

This program implements linear classification using the perceptron algorithm through MATLAB. The perceptron algorithm is employed to classify data points into two distinct categories based on a linear decision boundary. The implementation involves continuous adjustment of weights and threshold values during the training process to optimize classification accuracy. Using MATLAB for development allows for clear visualization of the algorithm's learning dynamics and decision boundary formation. The perceptron algorithm represents one of the most fundamental and classical machine learning approaches, with extensive applications in pattern recognition, data mining, and artificial intelligence. Key implementation aspects include the use of MATLAB's matrix operations for efficient weight updates and the implementation of convergence checks to ensure proper training termination. Through this program, users can gain deeper insights into the perceptron algorithm's underlying principles, performance characteristics, and practical applications in real-world problems.