Nearest neighbor-based techniques provide an approach to acoustic modeling that avoids the often lengthy and heuristic process of training traditional Gaussian mixturebased models. Here we study the problem of choosing the distance metric for a k-nearest neighbor (k-NN) phonetic frame classifier. We compare the standard Euclidean distance to two learned Mahalanobis distances, based on large-mar...

12:06 30th March 2004 Abstract The k -nearest neighbour (k -nn) model is a simple, popular classifier. Probabilistic k -nn is a more powerful variant in which the model is cast in a Bayesian framework using (reversible jump) Markov chain Monte Carlo methods to average out the uncertainy over the model parameters. The k -nn classifier depends crucially on the metric used to determine distances b...

Nearest-neighbour (NN) and k-nearest-neighbours (k-NN) techniques are widely used in many pattern recognition classification tasks. The linear approximating and eliminating search algorithm (LAESA) is a fast NN algorithm which does not assume that the prototypes are defined in a vector space; it only makes use of some of the distance properties (mainly the triangle inequality) in order to avoid...

The k-nearest neighbors classifier is one of the most widely used methods of classification due to several interesting features, such as good generalization and easy implementation. Although simple, it is usually able to match, and even beat, more sophisticated and complex methods. However, no successful method has been reported so far to apply boosting to k-NN. As boosting methods have proved ...

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, ...

conclusions by comparing the results of classification using multiple classifier fusion with respect to using each classifier separately, it is found that the classifier fusion is more effective in enhancing the detection accuracy. objectives through the improvement of classification accuracy rate, this work aims to present a computer-assisted diagnosis system for malaria parasite. materials an...

We present two related contributions of independent interest: (1) high-probability finite sample rates for k-NN density estimation, and (2) practical mode estimators – based on k-NN – which attain minimax-optimal rates under surprisingly general distributional conditions.

k-Nearest Neighbours (k-NN) is a well understood and widely-used approach to classification and regression problems. In many cases, such applications of k-NN employ the standard Euclidean distance metric for the determination of the set of nearest neighbours to a particular test data sample. This paper investigates the use of a data-driven evolutionary approach, named KTree, for the automatic c...

This report studies data-driven estimation of the directed information (DI) measure between twoem discrete-time and continuous-amplitude random process, based on the k-nearest-neighbors (k-NN) estimation framework. Detailed derivations of two k-NN estimators are provided. The two estimators differ in the metric based on which the nearest-neighbors are found. To facilitate the estimation of the ...