Nearest Neighbour
Consensus speed optimisation with finite leadership perturbation in k-nearest neighbour networks
... The newly presented semi-analytical methods (Power Op- timisation and Communities of Influence) leverage the first left eigenvector (FLE) of a graph’s adjacency matrix and manipulated versions of this matrix to ...
6
Bootstrap Inference for K-Nearest Neighbour Matching Estimators
... Abadie and Imbens (2008, Econometrica) showed that classical bootstrap schemes fail to provide correct inference for K-nearest neighbour (KNN) matching estimators of average causal effects. This is an ...
25
ANNF Estimation Technique and Methods for Finding nearest Neighbour Field
... Approximate Nearest Neighbors (ANN) for each patch in real or near real ...approximate nearest neighbor tools such as Locality Sensitive Hashing (LSH) or ...Approximate Nearest Neighbour Field ...
7
Discretisation of Data in a Binary Neural k-Nearest Neighbour Algorithm
... This paper evaluates several methods of discretisation (binning) within a k-Nearest Neighbour predictor. Our k-NN is constructed using binary neural networks which require continuous-valued data to be ...
31
Sales Forecasting using Linear Regression and K-Nearest Neighbour
... K nearest neighbour is a indispensable calculation that stores every accessible case and groups new cases dependent on a similitude measure ...its neighbour, with the case being assigned to the class ...
6
Bootstrap inference for K-nearest neighbour matching estimators
... a nearest neighbour classifier could also be used in our context to obtain valid inference, because the EICEs can be organized into clusters which are independent of each ...
28
Nearest neighbour models for local and regional avalanche forecasting
... the quality of the nearest neighbour search, avalanche observations must be reliable and complete. Days with- out observation should be marked, so neighbours are not misinterpreted as good days. This also ...
7
Limit theory for the random on-line nearest-neighbour graph
... The one-dimensional models considered in this paper (the ONG and the standard nearest- neighbour graph) are defined in terms of the spacings of points in the unit interval. Thus the theory of so-called ...
32
Asymptotic theory for the multidimensional random on-line nearest-neighbour graph
... on-line nearest-neighbour graph (ONG) is of particular theoretical interest since its total power-weighted length functional has both normal and non-normal limiting regimes, depending on the exponent ...
25
NEAREST NEIGHBOUR EXPANSION USING KEYWORD COVER SEARCH
... An improving number of the applications require the efficient execution of nearest neighbour (-NN) queries constrained by the properties of spatial objects. Due to the popularity of keyword search, ...
8
The use of synchrotron edge topography to study polytype nearest neighbour relationships in SiC
... The nearest neighbour interaction in this model is ferromagnetic while the next nearest neighbour is antiferromagnetic, the whole system is considered ...
27
Creating diverse nearest neighbour ensembles using simultaneous metaheuristic feature selection
... The nearest-neighbour (1NN) classifier has long been used in pattern recognition, exploratory data analysis, and data mining problems. A vital consideration in ob- taining good results with this technique ...
36
Heart Disease Prediction System Using K Nearest Neighbour Classification Technique
... The existing system using Support Vector Machine(SVM)[3], it propose a system for heart disease prediction. It was not provide accurate results and taks more time to train the database images[5]. The heart beat parameter ...
8
Scalable Distributed Processing of K nearest Neighbour Queries over Moving Objects
... ABSTRACT: Many applications involving moving objects is the task of processing k-nearest neighbour (k-NN) queries. Most of the existing approaches to this problem are designed for the centralized setting ...
6
Nearest-Neighbour-Induced Isolation Similarity and Its Impact on Density-Based Clustering
... To overcome these shortcomings of isolation trees, we propose to use a nearest neighbour partitioning mecha- nism which creates a Voronoi diagram (Aurenhammer 1991) where each cell is an isolating partition ...
8
A VARIATION OF NEAREST NEIGHBOUR ALGORITHM TO SOLVE SYMMETRIC TRAVELING SALESMAN PROBLEM
... Travelling salesman problem has been occupying as one of the interesting field of research relating to the real life problem which relates Hamiltonian graph. This is world unsolved problem known as NP-Complete problem. ...
9
Face Annotation Weakly Labelled Images using Nearest Neighbour Calculation
... representation, we extract the GIST features [13] to represent the extracted faces. As a result, each face can be represented as a d- dimensional vector. The fourth step of the framework is to index the extracted ...
6
Active learning via query synthesis and nearest neighbour search
... We compared our methods with random sampling, and uncertainty-based sampling method and LLR-based active learning [4]. i) Random sampling: The algorithm ran- domly selects an instance in each round of iteration.This is ...
10
A binary neural k-nearest neighbour technique
... its nearest neighbours and output the 100 near- est neighbours to a file on ...the nearest neighbour list of x if it is closer than the most distant neighbour in the current list as previously ...
19
Group based nearest neighbour in cervical cancer screening
... the nearest neighbour technique-was exploited to enable GBC ...group-based nearest neighbour techniques will also be evaluated against the existing conventional MACs approach that is summary ...
7