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 Developer:
Mike Gashler



Thanks to individuals who have made contributions to this project:

  • Desire' Gashler
  • Kevin Kemp
  • Helaman Ferguson
  • Roger Pack
  • Marcelo Hashimoto
  • Eric Moyer
  • Jean-Pierre Moreau
  • Olaf Krzikalla
  • Ivan Yanikov
  • Greg Sharp
  • Dmitriy Krylov


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

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

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

  34. How to submit a bug report or feature request
  35. Contributions that would benefit this project
  36. Getting the latest sources from our Subversion repository
  37. How to become a developer on this project
  38. How to cite Waffles