The K-nearest neighbor (KNN) classifier is one of the simplest and most common classifiers,
yet its performance competes with the most complex classifiers in the literature. The core of …

The K-nearest neighbor (KNN) classifier is one of the simplest and most common classifiers,
yet its performance competes with the most complex classifiers in the literature. The core of …

Local descriptors are widely used in face recognition due to their robustness to changes in
expression or occlusion in facial images. In this paper, a comparison of local descriptors …

The K-nearest neighbor (KNN) classifier is one of the simplest and most common classifiers,
yet its performance competes with the most complex classifiers in the literature. The core of …

This paper presents a new similarity measure to be used for general tasks including
supervised learning, which is represented by the K-nearest neighbor classifier (KNN). The …

Usually, in the deep learning community, it is claimed that generalized representations that
yielding outstanding performance/effectiveness require a huge amount of data for learning …

We showed in this work how the Hassanat distance metric enhances the performance of the
nearest neighbour classifiers. The results demonstrate the superiority of this distance metric …

This paper employs the methods from the design of experiments for supervised parameter
learning in image segmentation. We propose to use orthogonal arrays in order to keep the …

Although distance measures are used in many machine learning algorithms, the literature
on the context-independent selection and evaluation of distance measures is limited in the …

Financial fraud detection plays a crucial role in the stability of institutions and the economy at
large. Data mining methods have been used to detect/flag cases of fraud due to a large …