The main goal of the present study was to implement a hybrid pattern of cellular automata model and particle swarm optimization algorithm based on TM and ETM+ imagery of landsat satellite from 1988 to 2010 for simulating the urban sprawling. In this study, an alternative model was implemented in two ways: the first method was based on two images (1988 and 2010) and the second one was based on t...

Nearest neighbor searching is an important geometric subproblem in vector quanti-zation. Existing studies have shown that the diiculty of solving this problem eeciently grows rapidly with dimension. Indeed, existing approaches on unstructured codebooks in dimension 16 are little better than brute-force search. We show that if one is willing to relax the requirement of nding the true nearest nei...

Nearest neighbor searching is an important geometric subproblem in vector quantization. Existing studies have shown that the difficulty of solving this problem efficiently grows rapidly with dimension. Indeed, existing approaches on unstructured codebooks in dimension 16 are little better than brute-force search. We show that if one is willing to relax the requirement of finding the true neares...

In this research, two techniques of pixel-based and object-based image analysis were investigated and compared for providing land use map in arid basin of Mokhtaran, Birjand. Using Landsat satellite imagery in 2015, the classification of land use was performed with three object-based algorithms of supervised fuzzy-maximum likelihood, maximum likelihood, and K-nearest neighbor. Nine combinations...

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...

K-nearest neighbor (KNN) is an effective nonparametric classifier that determines the neighbors of a point based only on distance proximity. The classification performance KNN disadvantaged by presence outliers in small sample size datasets and its deteriorates with class imbalance. We propose local Bonferroni Mean Fuzzy K-Nearest Centroid Neighbor (BM-FKNCN) assigns label query dependent neare...

In this paper, we develop a novel Distance-weighted k -nearest Neighbor rule (DWKNN), using the dual distance-weighted function. The proposed DWKNN is motivated by the sensitivity problem of the selection of the neighborhood size k that exists in k -nearest Neighbor rule (KNN), with the aim of improving classification performance. The experiment results on twelve real data sets demonstrate that...

The k-nearest neighbor classifier follows a simple, yet powerful algorithm: collect the k data points closest to an unlabeled instance, according to a given distance measure, and use them to predict that instance’s label. The two components, the parameter k governing the size of used neighborhood, and the distance measure, essentially determine success or failure of the classifier. In this work...

BACKGROUND Individuals and families are relying more on food prepared outside the home as a source for at-home and away-from-home consumption. Restricting the estimation of fast-food access to fast-food restaurants alone may underestimate potential spatial access to fast food. METHODS The study used data from the 2006 Brazos Valley Food Environment Project (BVFEP) and the 2000 U.S. Census Sum...