The accuracy of k-nearest neighbor (kNN) classification depends significantly on the metric
used to compute distances between different examples. In this paper, we show how to learn …

We show how to learn a Mahanalobis distance metric for k-nearest neighbor (kNN)
classification by semidefinite programming. The metric is trained with the goal that the k …

A lane changing assistance system that advises drivers of safe gaps for making mandatory
lane changes at lane drops is developed. Bayes classifier and decision-tree methods were …

Hereby in this paper, we are interested to extraction methods and classification in case of
image classification and recognition application. We expose the performance of training …

Conventional k-nearest neighbor (KNN) classification approaches have several limitations
when dealing with some problems caused by the special datasets, such as the sparse …

Machine learning qualifies computers to assimilate with data, without being solely
programmed [1, 2]. Machine learning can be classified as supervised and unsupervised …

High dimension, low sample size (HDLSS) problems are numerous among real-world
applications of machine learning. From medical images to text processing, traditional …

The number of nearest neighbors K and the utilized distance measure considerably impact
the performance of the K-nearest neighbor (K-NN) algorithm. The information provided by …

To deal with noises in partial label learning (PLL), existing approaches try to perform
disambiguation either by identifying the ground-truth label or by averaging the candidate …

This paper presents a framework called localized support vector machine (LSVM) for
classifying data with nonlinear decision surfaces. Instead of building a sophisticated global …