One of the classical data mining techniques is k-nearest neighbor. This method uses the class of the k nearest neighbor to classify a new instance. The distance is calculated with one of the multiple mathematical distance metrics. In this paper, the technique is used in the air quality forecast domain in order to predict the value of the air quality index. This index is used to categorize the p...

Prostate segmentation is essential for calculating prostate volume, creating patient-specific prostate anatomical models and image fusion. Automatic segmentation methods are preferable because manual segmentation is timeconsuming and highly subjective. Most of the currently available segmentation methods use a priori knowledge of the prostate shape. However, there is a large variation in prosta...

The k-Nearest Neighbor (k-NN) classification method assigns to an unclassified point the class of the nearest of a set of previously classified points. A problem that arises when aplying this technique is that each labeled sample is given equal importance in deciding the class membership of the pattern to be classified, regardless of the typicalness of each neighbor. We report on the applicatio...

primary information about different methods of vegetation sampling is important to researchers to decide about their sampling. in this study we applied five distance methods (closest individual, nearest neighbor, random pairs, point-centered quarter and angle orderd) to estimate plant density in three vegetation types including artemisia sieberi, juncus littoralis and punica granatum in mazanda...

This paper presents a new method for viewpoint invariant pedestrian recognition problem. We use a metric learning framework to obtain a robust metric for large margin nearest neighbor classification with rejection (i.e., classifier will return no matches if all neighbors are beyond a certain distance). The rejection condition necessitates the use of a uniform threshold for a maximum allowed dis...

In the manifold learning problem one seeks to discover a smooth low dimensional surface, i.e., a manifold embedded in a higher dimensional linear vector space, based on a set of measured sample points on the surface. In this paper we consider the closely related problem of estimating the manifold’s intrinsic dimension and the intrinsic entropy of the sample points. Specifically, we view the sam...

Abstract The fuzzy k-nearest neighbor (FKNN) algorithm, one of the most well-known and effective supervised learning techniques, has often been used in data classification problems but rarely regression settings. This paper introduces a new, more general model. Generalization is based on usage Minkowski distance instead usual Euclidean distance. not optimal choice for practical problems, better...