Approximate nearest neighbor search is a technique which greatly reduces processing time and required amount of memory. Generally, there are the relationships of trade-off among accuracy, processing time and memory amount. Therefore, analysis on the relationships is an important task for practical application of the approximate nearest neighbor search method. In this paper, we construct a model...

Nearest neighbor rule or k-nearest neighbor rule is a technique of nonparametric pattern recognition. Its algorithm is simple and error is smaller than twice the Bayes error if there are enough training samples. However, it requires enormous computational quantities that is proportional to the number of samples and the number of dimensions of feature vector. In this paper, a fast algorithm for ...

Visitors are people who come to a place, entertainment, shopping, and tourism. one of the important factors for progress development place. With visitors, an tourism shopping area can develop. Therefore researchers will make study level visitor satisfaction. This research aims improve quality entertainment venue, increase quantity visitors. was conducted using K-Nearest Neighbor method. The met...

Abstract Bees Algorithm (BA) is a popular meta-heuristic method that has been used in many different optimization areas for years. In this study, new version of combinatorial BA proposed and explained detail to solve Traveling Salesman Problems (TSPs). The nearest neighbor was the population generation section BA, Multi-Insert function added local search instead Swap function. To see efficiency...

In this article, we will explore why Karlin-McGregor method of using orthogonal polynomials in the study of Markov processes was so successful for one dimensional nearest neighbor processes, but failed beyond nearest neighbor transitions. We will proceed by suggesting and testing possible fixtures.

This paper addresses the problem of certifying the performance of a precision flexure-base mechanism design with respect to the given constraints. Due to the stringent requirements associated with flexure-based precision mechanisms, it is necessary to be able to evaluate and certify the performance at the design stage, taking into account the possible sources of errors: such as fabrication tole...

Rare category detection is an open challenge for active learning, especially in the de-novo case (no labeled examples), but of significant practical importance for data mining e.g. detecting new financial transaction fraud patterns, where normal legitimate transactions dominate. This paper develops a new method for detecting an instance of each minority class via an unsupervised local-density-d...

In this work, we describe the main features of IFS-CoCo, a coevolutionary method performing instance and feature selection for nearest neighbor classifiers. The coevolutionary model and several related background topics are revised, in order to present the method to the ICPR’10 contest “Classifier domains of competence: The Landscape contest”. The results obtained show that our proposal is a ve...