A collection of command-line tools for researchers in machine learning, data mining, and related fields. All of the functionality is also provided in a clean C++ class library. Demo apps are included to show how to use the class library.
by: Mike Gashler

Builds on 32 and 64-bit platforms, including Linux, Windows, OSX, etc.
Distributed under the LGPL license.

If you're looking for a Java web framework, you probably want Waffle.
If you're looking for waffles recipes, try here.

Documentation
Change Log
Download
Forums



Main Developers:

Mike Gashler


Eric Moyer





Contributors:
(of bug-fixes, small improvements, helpful suggestions, etc.)

  • Desire' Gashler
  • Kevin Kemp
  • Helaman Ferguson
  • Roger Pack
  • Marcelo Hashimoto
  • Jean-Pierre Moreau
  • Olaf Krzikalla
  • Ivan Yanikov
  • Greg Sharp
  • Dmitriy Krylov
  • Will Bendick
  • Vojtech Rylko
  • Daniel McEnnis
  • Jonas Gehring
  • Andrew Borthwick
  • Vikram Rangnekar
  • Tang Yan



Thanks for the generous support from the Neural Networks and Machine Learning Lab at:



Thanks for the free project hosting provided by:
SourceForge.net Logo


Thanks to the GNU project for developing Linux, g++, other tools used to develop this code, and the LGPL license, which this project uses:
Gnu

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Some of the demo apps use:

Waffles Documentation / Tutorials

    Overviews and build instructions

  1. Overview of what is contained in the Waffles machine learning toolkit
  2. Building on Linux
  3. Building on OSX
  4. Building on Windows
  5. Using the waffles_wizard utility
  6. A big table showing the accuracy of our algorithms

    7. Using the command-line tools

  7. Full usage info for the waffles_audio app
  8. Full usage info for the waffles_cluster app
  9. Full usage info for the waffles_dimred app
  10. Full usage info for the waffles_generate app
  11. Full usage info for the waffles_learn app
  12. Full usage info for the waffles_plot app
  13. Full usage info for the waffles_recommend app
  14. Full usage info for the waffles_sparse app
  15. Full usage info for the waffles_transform app
  16. Data Formats
  17. Visualizing Data
  18. Examples of using supervised learning
  19. Dimensionality Reduction
  20. Collaborative filtering
  21. Document classification

    22. Programming with Waffles

  22. API Documentation for the GClasses library
  23. Getting started coding with Waffles
  24. Overview of the demo apps
  25. Representing your data with the GMatrix class
  26. Coding with supervised learners
  27. Developing a new learning algorithm
  28. Serialization
  29. Adding a new tool to Waffles
  30. Overview of the most useful classes for ML

    31. Contributing to Waffles

  31. How to submit a bug report or feature request
  32. Contributions that would benefit this project
  33. Getting the latest source code from our Git repository
  34. How to become a developer on this project
  35. How to cite Waffles