the aim of this work is to examine the feasibilities of the support vector machines (svms) and k-nearest neighbor (k-nn) classifier methods for the classification of an aquifer in the khuzestan province, iran. for this purpose, 17 groundwater quality variables including ec, tds, turbidity, ph, total hardness, ca, mg, total alkalinity, sulfate, nitrate, nitrite, fluoride, phosphate, fe, mn, cu, ...

We propose a nonparametric cluster algorithm based on local medians. Each observation is substituted by its local median and this new observation moves toward the peaks and away from the valleys of the distribution. The process is repeated until each observation converges to a fixpoint. We obtain a partition of the sample based on the convergence points. Our algorithm determines the number of c...

Computing the minimum spanning tree (MST) is a common task in the pattern recognition and the computer vision fields. However, little work has been done on efficient general methods for solving the problem on large datasets where graphs are complete and edge weights are given implicitly by a distance between vertex attributes. In this work we propose a generic algorithm that extends the classic...

Representing data as a linear combination of a set of selected known samples is of interest for various machine learning applications such as dimensionality reduction or classification. k-Nearest Neighbors (kNN) and its variants are still among the best-known and most often used techniques. Some popular richer representations are Sparse Representation (SR) based on solving an l1-regularized lea...

DEFINITION Given a set of n points and a query point, q, the nearest-neighbor problem is concerned with finding the point closest to the query point. Figure 1 shows an example of the nearest neighbor problem. On the left side is a set of n = 10 points in a two-dimensional space with a query point, q. The right shows the problem solution, s. Figure 1: An example of a nearest-neighbor problem dom...

While the K-Nearest-Neighbor (KNN) problem is well studied in the traditional wired, disk-based client-server environment, it has not been tackled in a wireless broadcast environment. In this paper, the problem of organizing location dependent data and answering KNN queries on air are investigated. The linear property of wireless broadcast media and power conserving requirement of mobile device...

We present a review of the literature on fast nearest neighbors using the basic approach from Karger and Ruhl [4] and a recent technique called cover trees. A small error in Insert procedure from the original paper on cover trees is corrected and an examination of how query time actually varies with the size of the problem is shown using a Python implementation of the basic cover tree algorithms.