in this paper, we give some new definitions of m-fuzzy metric spaces and we prove a common fixed point theorem for six mappings under the condition of weakly compatible mappings in complete m-fuzzy metric spaces.

Machine-learning solutions to classification, clustering and matching problems critically depend on the adopted metric, which in the past was selected heuristically. In the last decade, it has been demonstrated that an appropriate metric can be learnt from data, resulting in superior performance as compared with traditional metrics. This has recently stimulated a considerable interest in the to...

In this paper, we introduce a new entropy-like invariant, named Hausdorff metric entropy, for finitely generated semigroups acting on compact metric spaces from a set-valued view and study its properties. We establish the relation between Hausdorff metric entropy and topological entropy of a semigroup defined by Bis. Some examples with positive or zero Hausdorff metric entropy are given. Moreov...

In this paper, we discuss the existence and uniqueness of fixed points for $G$-asymptotic contractions in metric spaces endowed with a graph. The result given here is a new version of Kirk's fixed point theorem for asymptotic contractions in metric spaces endowed with a graph. The given result here is a generalization of fixed point theorem for asymptotic contraction from metric s paces to metr...

Let Xk be a sequence of independent and identically distributed random variables taking values in a compact metric space Ω, and consider the problem of estimating the law of X1 in a Bayesian framework. A conjugate family of priors for non-parametric Bayesian inference is the Dirichlet process priors popularized by Ferguson. We prove that if the prior distribution is Dirichlet, then the sequence...

In this paper, we present a new framework for the diagnostic prediction of transmitted speech quality. The idea is to extract perceptually relevant feature estimations from the speech signal, and combine them with an overall quality metric in order to obtain more reliable as well as more diagnostic predictions of speech quality. We implement this framework in two complementary ways: In terms of...

This paper presents a method for archiving and searching 3D Native American ceramic vessels using geometric modeling techniques. Archaeological vessels are scanned and defined as a set of three-dimensional triangulated meshes composed of points, edges and triangles. Our work includes modeling the data with parametric surfaces, extracting features to raise the level of abstraction of data, and o...

Target detection is one of the important elements of Automatic Target Recognition (ATR) systems. In this paper, we propose a new approach to detect outliers in radar returns, based on modelling the background using an empirical distribution rather than a parametric distribution. The key innovation lies in the use of the Characteristic Function (CF) to describe the distribution. The experimental...

In this paper we propose a novel method for learning a Mahalanobis distance measure to be used in the KNN classification algorithm. The algorithm directly maximizes a stochastic variant of the leave-one-out KNN score on the training set. It can also learn a low-dimensional linear embedding of labeled data that can be used for data visualization and fast classification. Unlike other methods, our...