Extreme Learning Machines
Brief Introduction to Extreme Learning Machines (ELM) Extreme Learning Machines (ELMs) are a type of feedforward neural network known for fast training speed and good generalization performance. The contain 2 active layers. A fixed random layer with activation functions and an entirely linear read-out layer. Here's a point-wise introduction: Type: Single-hidden layer feedforward neural networks (SLFNs). Key Idea: Input weights and biases in the hidden layer are randomly assigned and not updated during training. Training: The read-out layer weights are analytically determined using least squares (no iterative backpropagation). Single shot least square. Advantage: Extremely fast training compared to traditional neural networks. Application Areas: Regression, classification, clustering, and feature learning tasks. Limitation: Performance depends on hidden layer size and random initialization. Current State of the Art (as of 2025) ELM Variants: ...