In this paper, we address the metric learning problem utilizing a margin-based approach. Our metric learning problem is formulated as a quadratic semi-definite programming problem (QSDP) with local neighborhood constraints, which is based on the Support Vector Machine (SVM) framework. The local neighborhood constraints ensure that examples of the same class are separated from examples of differ...

An evidence theoretic classification method is proposed in this paper. In order to classify a pattern we consider its neighbours, which are taken as parts of a single source of evidence to support the class membership of the pattern. A single mass function or basic belief assignment is then derived, and the belief function and the pignistic (“betting rates”) probability function can be calculat...

Abstract: Problem Statement: Various concepts related to a smooth topological spaces have been introduced and relations among them studied by several authors (Chattopadhyay, Ramadan, etc). Conclusion/Recommendations: In this study, we presented the notions of three sorts of neighborhood structures of a smooth topological spaces and give some of their properties which are results by Ying extende...

As a research branch of data mining, clustering, as an unsupervised learning scheme, focuses on assigning objects in the dataset into several groups, called clusters, without any prior knowledge. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is one of the most widely used clustering algorithms for spatial datasets, which can detect any shapes of clusters and can automatic...

BACKGROUND Socioeconomic, demographic, and geographic factors are known determinants of stroke and myocardial infarction (MI) risk. Clustering of these factors in neighborhoods needs to be taken into consideration during planning, prioritization and implementation of health programs intended to reduce disparities. Given the complex and multidimensional nature of these factors, multivariate meth...

Personalized recommendation plays an important role in both e-commerce area and information filtering area. The neighborhood based collaborative filtering algorithm has already been used successfully. However, with the overwhelming explosion of Internet content, the problem of data sparsity has become more and more severe. The effect of data sparsity problem lies in both similarity computation ...

In this paper we present a neighborhood semantics for Intuitionistic Propositional Logic (IPL). We show that for each Kripke model of the logic there is a pointwise equivalent neighborhood model and vice versa. In this way, we establish soundness and completeness of IPL with respect to the neighborhood semantics. The relation between neighborhood and topological semantics are also investigated....

We discuss the use of online learning of the local search neighborhood. Specifically, we consider the Linkage Tree Genetic Algorithm (LTGA), a population-based, stochastic local search algorithm that learns the neighborhood by identifying the problem variables that have a high mutual information in a population of good solutions. The LTGA builds each generation a linkage tree using a hierarchic...

D. Molodtsov (1999) introduced the concept of a soft set as a new approach for modeling uncertainties. The aim of this work is to define special kinds of soft sets, namely soft, L-fuzzifying soft, L-soft, and L-fuzzy soft neighborhood sets and to use them in order to give an alternative characterization of categories related to topology: crisp topological, L-topological, L-fuzzifying topologica...

Connectivity is a fundamental concept that is widely utilized in spatial ecology. The majority of connectivity measures used in the recent ecological literature only consider the nearest neighbor patch/population, or patches within a limited neighborhood of the focal patch (a buffer). Meta-analysis suggests that studies using nearest neighbor connectivity measures are much less likely to find s...