Search

Abstract: This paper introduces a general framework for defining the entropy of a graph. Our definition is based on a local information graph and on information functionals derived from the topological structure of a given graph. More precisely, an information functional quantifies structural information of a graph based on a derived probability distribution. Such a probability distribution leads directly to an entropy of a graph. Then, the structural information content of a graph will be is interpreted and defined as the derived graph entropy. Another major contribution of this paper is the investigation of relationships between graph entropies. In addition to this, we provide numerical results demonstrating not only the feasibility of our method, which has polynomial time complexity, but also its usefulness with regard to practical applications aiming to an understanding of information processing in complex networks. [Copyright &y& Elsevier]

In this paper we analyze an optimal fourth-order family of methods suggested by Khattri and Babajee, (2013). We analyze the family using the information on the extraneous fixed points. Two measures of closeness to the imaginary axis of the set of extraneous points are considered and applied to the members of the family to find its best performer. The results are compared to three best members of King’s family of methods. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we proposed a new Multiple-Instance Learning (MIL) method based on nonparallel classifier (called MI-NSVM). The method is mainly divided into two steps. The first step is to generate a spare hyperplane and estimate the score of each instance in positive bags. For the second step, MI-NSVM seeks the “most positive” instance of each positive bag by the information obtained in the first step, and then generates the second hyperplane. MI-NSVM is a useful extension of twin SVM and has the same advantages as it. All experiments show that our method is superior to the traditional MI-SVM and MI-TSVM in both computation time and classification accuracy. [Copyright &y& Elsevier]

Abstract: In this paper scheduling communications in data gathering networks is analyzed. We study collecting information by a set of sensors, each of which stores the data in its memory buffer and then passes them to a base station. The network lifetime ends as soon as the first node is out of memory. We use a divisible load model to propose a communication scheduling algorithm that maximizes the system lifetime, and hence, the total amount of gathered information. The influence of the sensor communication rates on the obtained schedules is exposed in a series of computational experiments. [Copyright &y& Elsevier]

Abstract: Nonadditive measure is a generalization of additive probability measure. Sugeno integral is a useful tool in several theoretical and applied statistics which has been built on non-additive measure. Integral inequalities play important roles in classical probability and measure theory. The classical Berwald integral inequality is one of the famous inequalities. This inequality turns out to have interesting applications in information theory. In this paper, Berwald type inequality for the Sugeno integral based on a concave function is studied. Several examples are given to illustrate the validity of this inequality. Finally, a conclusion is drawn and a problem for further investigations is given. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a method for establishing relations between entropy-based measures applied to graphs. A special class of relations called implicit information inequalities or implicit entropy bounds is developed. A number of entropy-based measures of the structural information content of a graph have been developed over the past several decades, but little attention has been paid to relations among these measures. The research reported here aims to remedy this deficiency. [Copyright &y& Elsevier]