Explore MATLAB source code curated for "极限学习机" with clean implementations, documentation, and examples.
Implementation of differential evolution algorithm to optimize extreme learning machines, resulting in significantly improved diagnostic accuracy through systematic parameter tuning and neural network enhancement.
Enhancing ELM accuracy through Particle Swarm Optimization (PSO) parameter tuning, featuring algorithm implementation insights and performance benefits for large-scale datasets
Comparative analysis of ELM (Extreme Learning Machine) and ELM_2 algorithms with source code implementations including EELM (published in Neural Computing by Yuguang Wang) and YELM (enhanced ELM version published by Yuboyuan). This program provides performance benchmarking for these three machine learning approaches. For theoretical background, please refer to the authors' publications. To execute the comparison, simply run the pendigits_mean.m main file which handles data preprocessing, model training, and performance evaluation.
Extreme Learning Machine (ELM) based classification for imbalanced datasets, delivering exceptional performance with fast processing speeds
MATLAB toolbox for Optimal Pruned Extreme Learning Machine with complete source code ready for modification and implementation
Utilizing the Bat Algorithm (BA) to optimize Extreme Learning Machine (ELM) input weights and biases significantly improves diagnostic accuracy. This implementation involves iterative parameter tuning through BA's echolocation-inspired search mechanism, enhancing ELM's generalization capability via swarm intelligence optimization techniques.
Extreme Learning Machine-based prediction model utilizing chaotic data for enhanced accuracy and robustness
Extreme Learning Machine (ELM) is a neural network simulation technique that offers faster learning speeds compared to Backpropagation (BP) and Sequential Minimal Optimization (SVM), with simpler parameter configuration requirements.
Extreme Learning Machine (ELM) is an efficient and user-friendly learning algorithm for Single-hidden Layer Feedforward Neural Networks (SLFNs). Proposed by Associate Professor Guang-Bin Huang at Nanyang Technological University in 2006, ELM eliminates the need for manual hyperparameter tuning common in traditional neural network algorithms like Backpropagation (BP). The algorithm requires only the specification of hidden layer nodes, automatically determines input weights and biases, and guarantees unique optimal solutions with exceptional learning speed and generalization performance. Key implementation involves random weight initialization and Moore-Penrose pseudoinverse computation for output weight derivation.
Extreme Learning Machine is a faster algorithm compared to Support Vector Machines, featuring efficient training through randomized parameter initialization and applications in large-scale data processing.