We develop a general framework for margin-based multicategory classification in metric spaces. The basic work-horse is a margin-regularized version of the nearest-neighbor classifier. We prove generalization bounds that match the state of the art in sample size n and significantly improve the dependence on the number of classes k. Our point of departure is a nearly Bayes-optimal finite-sample r...

In this paper we propose and explore the k-Nearest Neighbour UCB algorithm for multiarmed bandits with covariates. We focus on a setting where the covariates are supported on a metric space of low intrinsic dimension, such as a manifold embedded within a high dimensional ambient feature space. The algorithm is conceptually simple and straightforward to implement. The k-Nearest Neighbour UCB alg...

Nearest Neighbour algorithms for pattern recognition have been widely studied. It is now well-established that they offer a quick and reliable method of data classification. In this paper we further develop the basic definition of the standard k-nearest neighbour algorithm to include the ability to resolve conflicts when the highest number of nearest neighbours are found for more than one train...

Grandemultipara are women who give birth more than four times usually experience complications in pregnancy and childbirth. or better known as mothers 4 can cause various that will be experienced by the mother, one of which is bleeding. pregnancies included high-risk pregnancies, because occur either during The K-NN algorithm applied process diagnosing human diseases, so several references take...

This paper proposes a nearest neighbor classifier design method based on vector quantization (VQ). By investigating the error distribution pattern of the training set, the VQ technique is applied to generate prototypes incrementally until the desired classification result is reached. Experimental results demonstrate the effectiveness of the method. 2004 Elsevier B.V. All rights reserved.