We describe the voice quality classification system that uses entropy and dynamism criteria as discrimination features. The main idea of this approach is that the input neural net is considered as an informational channel. Channel tuned to the certain type of information transmits it best of all according to the informational criterion. In our case a multilayer perceptron (MLP) emitted posterio...

By combining the explicit formula of the Shannon informational entropy ) , ( Y X H for two random variables X and Y , with the entropy )) ( ( X f H of ) (X f where (.) f is a real-valued differentiable function, we have shown that the density of the amount of information in Shannon sense involved in a non-random differentiable function is defined by the logarithm of the absolute value of its de...

This paper assumes the cell formation problem as a distributed decision network. It proposes an approach based on application and extension of information theory concepts, in order to analyze informational complexity in an agent- based system, due to interdependence between agents. Based on this approach, new quantitative concepts and definitions are proposed in order to measure the amount of t...

The study is devoted to definition of generalized metrical and topological (informational entropy) characteristics of neural signals via their well-known theoretical models. We have shown that time dependence of action potential of neurons is scale invariant. Information and entropy of neural signals have constant values in case of self-similarity and self-affinity.

Understanding query ambiguity in web search remains an important open problem. In this paper we reexamine query ambiguity by analyzing the result clickthrough data. Previously proposed clickthrough-based metrics of query ambiguity tend to conflate informational and ambiguous queries. To distinguish between these query classes, we introduce novel metrics based on the entropy of the click distrib...

Thermodynamic entropy is not an entirely satisfactory measure of information of a quantum state. This entropy for an unknown pure state is zero, although repeated measurements on copies of such a pure state do communicate information. In view of this, we propose a new measure for the informational entropy of a quantum state that includes information in the pure states and the thermodynamic entr...

It has long been appreciated that Shannon information can in principle be used to measure the informational content of sensory signals. But the lack of a general, context-sensitive method for modelling signal probability distributions has obstructed practical applications. Addressing this problem, the paper introduces an approach based on dependency models. Using Shannon’s concept of conditiona...