Shortly after I’d posted yesterday’s A Slew of Dmitry Kazakov Updates news, a release announcement for version 1.0 of his Fuzzy Machine Learning Framework program was made to comp.lang.ada. The Fuzzy Machine Learning Framework allows us to operate uncertain and contradictory data in a unified, certain and feasible way. I’m sure you know that I did not come up with that last sentence. I found it on the website for the project, where this quote is also from:
Based on the intuitionistic fuzzy sets and the possibility theory. Fuzzy approach is fully utilized by using intuitionistic sets extended to represent not only uncertain, but also contradictory data within same framework. Consequently the results of the possibility theory are generalized for the case of contradictory data. Same as with the shades of uncertainty we introduce the shades of contradiction from feasible to infeasible. This allows us to operate uncertain and contradictory data in a unified, certain and feasible way.
Fuzzy features. All the data the system operates on are considered fuzzy. For this a notion of fuzzy feature is introduced as a generalization of crisp features known in classical machine learning. Similar to statistical pattern recognition, a feature is a measurable function with the possibility used as the measure. Both the features and the things viewed through features are consistently supposed fuzzy. By these two ways uncertainty comes into play. It might be a crisp thing described in vague terms, like a number being big or small. Or it can be a precise description of something uncertain, like a temperature said to be n degrees. There could be a mixture of both.
I gotta admit that most of that text is quite a bit above my pay grade, but I’m sure that if you have a need for fuzzy machine learning, then you probably “get” exactly what he’s talking about. You can read the full release announcement here.