Feature Extraction CUDA ELM Documentation¶

GPU-accelerated Extreme Learning Machine feature extraction with composable feature maps, solver strategies, and CPU/GPU backends.

Sections¶

Getting started¶

• Getting started
• Quickstart
• Choosing a model

Concepts and architecture¶

• Architecture
• Batch ELM
• OS-ELM
• ReOS-ELM and FOS-ELM
• OS-CELM
• ELM-AE
• ML-ELM
• RBF features

Operations and contributor guides¶

• Deployment
• Troubleshooting
• Building
• Testing
• Style
• Demos
• Benchmarks
• Migration from v1 to v2
• Glossary
• Roadmap

License and citation¶

• License – MIT License
• Citation Guide – How to cite this project and underlying algorithms

Tech stack mindmap¶

mindmap
  root((Feature Extraction CUDA ELM))
    Core
      C++20
      CMake
      GoogleTest
    Pipeline
      FeatureMap
      Solver
      Backend
    Algorithms
      Batch ELM
      OS-ELM
      ELM-AE
      ML-ELM
      RBF
    GPU
      CUDA 13.x
      cuBLAS
      cuSOLVER
      Thrust
    Docs
      MkDocs Material
      Doxygen
      Mermaid

References¶

• Huang, Guang-Bin, Qin-Yu Zhu, and Chee-Kheong Siew. 2006. Extreme learning machine: theory and applications.
• Liang, Nan-Ying, Guang-Bin Huang, P. Saratchandran, and N. Sundararajan. 2006. A fast and accurate online sequential learning algorithm for feedforward networks.
• Kasun, L. L. C., Yang Yang, Guang-Bin Huang, and Zhiping Zhou. 2013. Extreme learning machine for multilayer perceptron and autoencoder feature learning.
• Broomhead, David S., and David Lowe. 1988. Multivariable functional interpolation and adaptive networks.
• Moody, John, and Christian J. Darken. 1989. Fast learning in networks of locally-tuned processing units.