𝔖 Bobbio Scriptorium
✦   LIBER   ✦

A fuzzy approach to some classification problems

✍ Scribed by A. Gisolfi; P. di Donato


Publisher
John Wiley and Sons
Year
1993
Tongue
English
Weight
580 KB
Volume
8
Category
Article
ISSN
0884-8173

No coin nor oath required. For personal study only.

✦ Synopsis


An appropriate algebraic structure was previously defined which can be regarded as a possible alternative to the theory of approximate reasoning, [A. Gisolfi, Fuzzy Sets Syst. 44, 37-43 (1992)l. In this article we aim at extending the operations of the structure in order to cope with classification problems. The theoretical aspects are emphasized in order to give an adequate background to the possible applications. After defining the basic elements and the related operations, the structure is implemented by means of Prolog. Finally the relationship between the structure and the problems of classification is discussed in some detail.


📜 SIMILAR VOLUMES


A Bayesian Decision Theory Approach to C
✍ Richard A. Johnson; Abderrahmane Mouhab 📂 Article 📅 1996 🏛 Elsevier Science 🌐 English ⚖ 373 KB

We address the classification problem where an item is declared to be from population ? j if certain of its characteristics v are assumed to be sampled from the distribution with pdf f j (v | % j ), where j=1, 2, ..., m. We first solve the two population classification problem and then extend the re

A neural network approach to the classif
✍ James W. Denton; Ming S. Hung; Barbara A. Osyk 📂 Article 📅 1990 🏛 Elsevier Science 🌐 English ⚖ 612 KB

The task of classifying observations into known groups is a common problem in decision making. A wealth of statistical approaches, commencing with Fisher's linear discriminant function, and including variations to accommodate a variety of modeling assumptions, have been proposed. In addition, nonpar

Fuzzy heterogeneous neurons for imprecis
✍ Julio J. Valdés; Lluís A. Belanche; René Alquézar 📂 Article 📅 2000 🏛 John Wiley and Sons 🌐 English ⚖ 166 KB

In the classical neuron model, inputs are continuous real-valued quantities. However, in many important domains from the real world, objects are described by a mixture of continuous and discrete variables, usually containing missing information and uncertainty. In this paper, a general class of neur