Extreme Learning Machine (ELM): Algorithm Overview and Implementation

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 (ELM): A Fast Neural Network Algorithm for Single-Hidden Layer Feedforward Networks

Extreme Learning Machine (ELM) is a simple, efficient learning algorithm for Single-hidden Layer Feedforward Networks (SLFNs) proposed by Associate Professor Guang-Bin Huang from Nanyang Technological University in 2006. Unlike traditional neural network training algorithms (e.g., Backpropagation), ELM requires minimal parameter tuning—only the number of hidden nodes needs specification—and avoids local optima by randomly initializing input weights and biases without iterative adjustments. The algorithm computes output weights analytically via Moore-Penrose pseudoinverse, ensuring unique optimal solutions and delivering rapid training with strong generalization. Code implementations typically involve random weight initialization, hidden layer activation (e.g., sigmoid), and linear output solving.