Extreme Learning Machine for Regression Fitting and Classification with Implementation Examples

This article describes the application of Extreme Learning Machine (ELM) in regression fitting and classification tasks, containing implementation code for gasoline octane prediction based on near-infrared spectroscopy and iris species identification. The content provides detailed explanations of ELM's working mechanism and its practical implementation approaches for solving real-world problems. The implementation typically involves: - Data preprocessing techniques including normalization and feature scaling - ELM model architecture with random hidden layer weights and bias initialization - Moore-Penrose pseudoinverse computation for output weight calculation - Model training process with single-step learning algorithm - Performance evaluation using metrics like RMSE for regression and accuracy for classification We discuss detailed aspects of data preprocessing, model training, and evaluation procedures, along with practical implementation tips and suggestions. The code structure demonstrates: - Input layer configuration handling spectral data or feature vectors - Hidden layer activation functions (sigmoid, RBF, or sine functions) - Output layer design for regression (continuous values) or classification (categorical labels) - Cross-validation techniques for model robustness assessment By studying this material, you will gain comprehensive understanding of ELM applications and enhance your machine learning skills. Additionally, we provide supplementary resources and learning opportunities to help you master this field more effectively.