Logical analysis of data (LAD) is one of the methodologies for extracting knowledge as a Boolean function f from a given pair of data sets (T; F ) on attributes set S of size n, in which T (resp., F ) f0; 1g n denotes a set of positive (resp., negative) examples for the phenomenon under consideration. In this paper, we consider the case in which extracted knowledge has a decomposable structure;...

Verbal idioms can be divided into two main groups: non-compositional idioms as kick the bucket and composit ional /decomposable idioms as spill the beans. In the following we will point to the fact that there are German decomposable idioms which can be decomposed into components, having identifiable meanings contributing to the meaning of the whole. These idiom compoimnts are taken to have refe...

Decomposition analysis is a critical tool for understanding the social and spatial dimensions of inequality, segregation, and diversity. In this paper, I propose a new measure – the Divergence Index – to address the need for a decomposable measure of segregation. Although the Information Theory Index has been used to decompose segregation within and between communities, I argue that it measures...

According to Gestalt theory, any image is perceived subconsciously as a coherent structure (or whole) with two contrast substructures: figure and ground. The consists of numerous autogenerated substructures an inherent hierarchy far more smalls than larges. Through these substructures, the structural beauty (L), or equivalently livingness space, can be computed by multiplication number (S) thei...

the traditional event tree analysis uses a single probability to represent each top event. however, it is unrealistic to evaluate the occurrence of each event by using a crisp value without considering the inherent uncertainty and imprecision a state has. the fuzzy set theory is universally applied to deal with this kind of phenomena. the main purpose of this study is to construct an easy metho...

A measure of s k c e s s for any learning algorithm is how u s e ful it is in a variety of learning situations. Those learning algorithms that support universal function approximation can theoretically h e applied to a very large and interesting class of learning problems. Many kinds of neural network architectures have already been shown to support universal approximation. In this paper, we wi...