The (k-)nearest neighbor searching has very high computational costs. The algorithms presented for nearest neighbor search in high dimensional spaces have have suffered from curse of dimensionality, which affects either runtime or storage requirements of the algorithms terribly. Parallelization of nearest neighbor search is a suitable solution for decreasing the workload caused by nearest neigh...

abstract saturated hydraulic conductivity (ks) is needed for many studies related to water and solute transport, but often cannot be measured because of practical and/or cost-related reasons. nonparametric approaches are being used in various fields to estimate continuous variables. one type of the nonparametric lazy learning algorithms, a k-nearest neighbor (k-nn) algorithm, was introduced and...

kernel density estimators are the basic tools for density estimation in non-parametric statistics.  the k-nearest neighbor kernel estimators represent a special form of kernel density estimators, in  which  the  bandwidth  is varied depending on the location of the sample points. in this paper‎, we  initially introduce the k-nearest neighbor kernel density estimator in the random left-truncatio...

This paper presents a method for overcoming time-varying co-channel interference (CCI) using type-2 fuzzy adaptive filters (FAF). The type-2 FAF is realized using an unnormalized type-2 Takagi–Sugeno–Kang fuzzy logic system. A clustering method is used to adaptively design the parameters of the FAF. We use transversal equalizer and decision feedback equalizer structures to eliminate the CCI. Si...

A text-dependent speaker recognition method is proposed using trapezoidal fuzzy similarity function to measure the similarity of voice features between a test user and the registered speaker who has nearest distance. The trapezoidal fuzzy similarity function is constructed based on three-time data recorded during enrolment process as personal identification voice (PIV) and statistical data of a...

Finding Nearest Neighbors efficiently is crucial to the design of any nearest neighbor classifier. This paper shows how Layered Range Trees could be used for efficient nearest neighbor classification. The presented algorithm is simple and finds the nearest neighbor in a logarithmic order. It performs d log n + k distance measures to find the nearest neighbor, where k is a constant that is much ...

In this paper a new approach to fault diagnosis in an AC motor is introduced. This system combines a neuro-fuzzy system called FasArt (Fuzzy Adaptive System ART based) and the well-known fuzzy k nearest neighbor algorithm. A set of 15 types of non destructive faults has been tested, reaching a high degree of early fault detection and fault type recognition. Moreover, taking into account the neu...

This paper presents a simple, but computationally efficient approach for fingerprint recognition. In this proposed approach fingerprint image is divided into windows of size 3  3 to extract the fuzzy features. The information at the center of the window is the product of information extracted using Local Binary Pattern (LBP) and fuzzy membership function. Maximization of mutual information bet...

In this paper, in order to improve recognition rate of the Jing Hua honey by adding different proportions of glucose, the pattern recognition methods of Radial Basis Function (RBF), Fuzzy k-nearest neighbor algorithm (FKNN) and Fuzzy Adaptive Resonance Theory MAP (Fuzzy ARTMAP) were used to classify the different honey adulterated proportion. The result shows that the recognition effect by usin...