v1.3-JMLR

This release corresponds to the version of the software accepted in Journal of Machine Learning Research

Changelog:

• Reorganized folders and renamed scripts
• Tutorials migrated to Jupyter notebooks
• Added tests for code and tutorials
• Added continuous integration with travis
• Added datasets and table of dataset characteristics
• Fixed language typos
• Fixed hyperlinks in documentation
• Refactored runAlgorithm to fitpredict
• Tested on Octave 4.4
• Easier installation process
• Bugs corrected related to key/value parameters processing in several methods
• Added different link functions to POM
• Added new methods: LIBLINEAR and HPOLD
• Added option to perform reports with the sum of generalization matrices
• Suppressed most compilation warnings
• Homogenize shape of matrix in model files
• Parameter selection can be done from the API
• INI files allow defining multiple experiments of different methods

v1.2

News:

• Bugs corrected related to key/value parameters processing in several methods
• Added different link functions to POM
• Added new methods: LIBLINEAR and HPOLD
• Added option to do reports with the sum of generalization matrices
• Suppressed compilation warnings
• Homogenize shape of matrix in model files
• Parameters selection can be done from the API
• Refactor of fit/predict API
• Added example to add new methods
• Typos fixed in web and tutorials

v1.1

• Fixed several issues:
  • Fixed many errors.
  • RBF parameter is only allowed for KDLOR since it was not effective in other methods
  • Bugs with condor and ini files
  • Modified inifile to be case sensitive with keys.
  • Types defined in ini files have to match the object properties types.
• Added Key/value parameteres for Algorithm constructors
• Parameters validation are now completely done in Algorithm, there Experiments is now Algorithm type agnostic.
• runAlgorithm receives and structure of parameters
• Updated documentation with three tutorials
• Added more example datasets
• Added code and ini examples
• Added NNPOM and NNOP methods.

v1.0

First release with many errors corrected since the survey paper publication. The roadmap for version 1.0 is almost complete (https://github.com/ayrna/orca/wiki). Main new features:

• Simplified installation
• Instalation test scripts
• INI format for experiments configuration
• Ported to Octave, including parallelization
• Ported to Windows
• Adapted to last Matlab releases
• Many error handling included
• Algorithms API homogenization
• Code cleaned and comments follow matlab style guide.