Name
Abstract | ||
|---|---|---|
In this paper we propose a random set framework for learning linguistic models for prediction problems. We show how we can model prediction problems based on learning linguistic prototypes deflned using joint mass assignments on sets of labels. The potential of this approach is then demonstrated by its application to a model and by benchmark problem and comparing the results obtained with those from other state-of-the-art learn- ing algorithms. |
Year | Venue | Field |
|---|---|---|
2003 | EUSFLAT Conf. | Neuro-fuzzy,Defuzzification,Fuzzy classification,Fuzzy set operations,Fuzzy logic,Fuzzy mathematics,Fuzzy set,Artificial intelligence,Fuzzy number,Machine learning,Mathematics |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
6 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nicholas J. Randon | 1 | 0 | 1.01 |
| Jonathan Lawry | 2 | 172 | 19.06 |