It is well known that for Gaussian channels, a nearest neighbor decoding rule, which seeks the minimum Euclidean distance between codeword and received channel output vector, maximum likelihood solution hence capacity-achieving. Nearest remains convenient yet mismatched general key message of this paper performance can be improved by generalizing its metric to incorporate state dependent proces...

Given a set S of n data points in some metric space. Given a query point q in this space, a nearest neighbor query asks for the nearest point of S to q. Throughout we will assume that the space is real d-dimensional space <d, and the metric is Euclidean distance. The goal is to preprocess S into a data structure so that such queries can be answered efficiently. Nearest neighbor searching has ap...

Nearest neighbor is one of the most used techniques for performing classification tasks. However, its simplest version has several drawbacks, such as low efficiency, storage requirements and sensitivity to noise. Prototype generation is an appropriate process to alleviate these drawbacks that allows the fitting of a data set for nearest neighbor classification. In this work, we present an exten...

We present Stochastic Neighbor Compression (SNC), an algorithm to compress a dataset for the purpose of k-nearest neighbor (kNN) classification. Given training data, SNC learns a much smaller synthetic data set, that minimizes the stochastic 1-nearest neighbor classification error on the training data. This approach has several appealing properties: due to its small size, the compressed set spe...

A k-nearest neighbor (kNN) query determines the k nearest points, using distance metrics, from a given location. An all k-nearest neighbor (AkNN) query constitutes a variation of a kNN query and retrieves the k nearest points for each point inside a database. Their main usage resonates in spatial databases and they consist the backbone of many location-based applications and not only. In this w...

Received Aug 26, 2015 Revised Nov 19, 2015 Accepted Dec 7, 2015 Unsupervised leaning is a popular method for classify unlabeled dataset i.e. without prior knowledge about data class. Many of unsupervised learning are used to inspect and classify network flow. This paper presents in-deep study for three unsupervised classifiers, namely: K-means, K-nearest neighbor and Expectation maximization. T...

The nearest neighbor method together with the dynamic time warping (DTW) distance is one of the most popular approaches in time series classification. This method suffers from high storage and computation requirements for large training sets. As a solution to both drawbacks, this article extends learning vector quantization (LVQ) from Euclidean spaces to DTW spaces. The proposed LVQ scheme uses...