Extreme Learning Machine Source Code by Huang Guangbin - MATLAB Implementation

The Extreme Learning Machine (ELM) source code, developed by Professor Huang Guangbin at Nanyang Technological University, serves as an excellent educational resource for learning MATLAB programming and machine learning implementation. This codebase features a comprehensive step-by-step approach that guides beginners through the core ELM algorithm while demonstrating proper MATLAB coding practices. The implementation includes key functions for random weight initialization, hidden layer computation using activation functions (like sigmoid or ReLU), and analytical solution for output weights through Moore-Penrose pseudoinverse. The code structure showcases efficient matrix operations characteristic of MATLAB, with clear comments explaining each computational step. Beginners can study how the code handles data preprocessing, model training with single-hidden layer feedforward networks, and prediction mechanisms. The implementation demonstrates important MATLAB concepts including vectorization, matrix manipulation, and function organization. Through hands-on experimentation with this code, users gain practical experience in implementing machine learning algorithms while developing solid MATLAB programming foundations. This resource is valuable for both students seeking to enhance their technical skills and professionals looking to understand ELM algorithm implementation details, providing real-world applications of neural network concepts with mathematically grounded solutions.