Fuzzy machine learning documentation
Author: Dmitry Kazakov

|  | Application programming interface. This document contains a programmer's guide to the fuzzy machine learning system. It provides a description of the Ada packages comprising the machine learning system core and examples of use. If you want to use the framework programmatically, as a library, or extend it, you have to read this document.
|  | Fuzzy control language. This document contains the specification of the intuitionisitc extension of the Fuzzy Control Language used by the fuzzy machine learning system. The extension is compatible with the draft IEC 1131 with the following goals in mind: Full support of intuitionistic fuzzy sets as an extension of plain fuzzy sets on the case of incomplete and contradictory data. Intuitionistic fuzzy sets natively express the measurement errors as found in physical systems; Integrated support of dimensioned values in SI (Le Système International d'Unités) unit system. Support for irregular dimension units and ones outside SI; The language should represent a fully functional consistent subset of FCL as specified in the draft.
|  | Graphical user interface. The graphical user interface is a GTK+ front end to the fuzzy machine learning system, which can be used to conduct experiments without programming. This document is a user guide to the interface.
|  | On intuitionistic fuzzy machine learning. The paper that laid the foundations of the intuitionistic machine learning used in the fuzzy machine learning system. Abstract: The aim of this work is to propose an intuitionistic formulation of the machine learning problem, completely independent from probability theory. Intuitionistic fuzzy sets naturally appear in machine learning based on possibility theory as a result of uncertainty in the measuring process. Considering this only type of uncertainty, a notion of fuzzy feature as a mapping. can be defined. Fuzzy features originate four different pattern spaces. Fuzzy classification is defined as a pair of images in the pattern spaces. Properties of the fuzzy pattern spaces required for fuzzy inference are demonstrated.