MATLAB-Based Extreme Learning Machine (ELM) Algorithms for Classification and Regression Tasks

This article explores MATLAB-based Extreme Learning Machine (ELM) algorithms for classification and regression tasks. ELM represents a fast and efficient machine learning approach that enables high-performance classification and regression on large datasets while avoiding overfitting issues. Compared to other machine learning algorithms, ELM demonstrates superior training speed and enhanced generalization performance. We delve into ELM's working mechanism, including its application in both classification and regression scenarios. The implementation covers MATLAB coding techniques for ELM, featuring key functions like randomized hidden layer weight initialization and analytical output weight calculation using Moore-Penrose pseudoinverse. Sample code examples illustrate practical implementation aspects, including data preprocessing, model training with single-hidden-layer feedforward networks, and prediction functions. Finally, we examine ELM's real-world application prospects while discussing potential limitations and future improvement directions, such as optimized activation function selection and regularization techniques for enhanced stability.