fuzzy machine learning framework

We have hosted the application fuzzy machine learning framework in order to run this application in our online workstations with Wine or directly.

Quick description about fuzzy machine learning framework:

Fuzzy machine learning framework is a library and a GUI front-end for machine learning using intuitionistic fuzzy data. The approach is based on the intuitionistic fuzzy sets and the possibility theory. Further characteristics are fuzzy features and classes; numeric, enumeration features and features based on linguistic variables; user-defined features; derived and evaluated features; classifiers as features for building hierarchical systems; automatic refinement in case of dependent features; incremental learning; fuzzy control language support; object-oriented software design with extensible objects and automatic garbage collection; generic data base support through ODBC or SQLite; text I/O and HTML output; an advanced graphical user interface based on GTK+; and examples of use.

Features:

• Based on the intuitionistic fuzzy sets and the possibility theory
• All the data the system operates on are considered fuzzy
• Fuzzy classes, within the framework classes take a natural interpretation of distinguished features
• Numeric, enumeration features and features based on linguistic variables
• Open for definition of new features beyond built-in classes of numeric, nominal and linguistic ones
• Derived and evaluated features. Along with the measured features the system supports the features deduced from other features
• Classifiers as features for building hierarchical systems
• Automatic refinement in case of dependent features
• Incremental learning support
• Extended fuzzy control language support
• Object-oriented software design
• Features, training sets and classifiers are extensible objects
• Automatic garbage collection
• Generic data base support through ODBC and SQLite
• Ada 95, 2005, 2012 compliant. GTK+ GUI requires at least Ada 2005
• Text I/O is provided for teaching sets and classifiers. Teaching sets can be imported in an intuitive format from text files
• Training sets and classifiers can be output in directly HTML format, supporting a web-ready solution
• GTK+ 3 GUI. The GUI is optional the system can be used fully programmatically
• Delivered with an set of samples varying from ones illustrating usage of the system components to examples of training on real- life and size data

Audience: Science/Research, Education, Advanced End Users, Developers.
User interface: GTK+.
Programming Language: Ada.
Database Environment: SQLite, ODBC.

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