k-nearest neighbour
Consensus speed optimisation with finite leadership perturbation in k-nearest neighbour networks
... to k-nearest neighbour (k-NNR) connection rules, by allocating a finite leadership resource amongst selected ...perturbed k-NNR network is identi- fied as the largest element of the ...
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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 ...
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Heart Disease Prediction System Using K Nearest Neighbour Classification Technique
... There is no scarcity of records regarding medical problems of patients regarding heart strokes. However the potential they have- to help us predict similar possibilities in apparently healthy adults are going unnoticed. ...
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Bootstrap inference for K-nearest neighbour matching estimators
... -nearest neighbour (KNN) matching estimators (Abadie and Imbens, 2006) are popular for estimating, non-parametrically, the average causal effect of a binary variable on an outcome in observational studies, ...
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Enhancing the Classification Accuracy of Noisy Dataset By Fusing Correlation Based Feature Selection with K-Nearest Neighbour
... the k-NN algorithm is sensitive to irrelevant attributes therefore its performance degrades significantly when a dataset contains missing values or noisy ...the k-NN algorithm can be minimized when combined ...
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A K-Nearest Neighbour Algorithm-Based Recommender System for the Dynamic Selection of Elective Undergraduate Courses
... with k-nearest Neighbour search to find closely correlated users, and content filtering based on the most popular courses with older students and which teachers the user has shown a preference ...
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Extreme Precipitation Events Simulation Using An Improved K-Nearest Neighbour Weather Generating Model
... upon K- nearest neighbor algorithm do not produce new values but merely reshuffles the historical data to generate realistic weather ...allows nearest neighbour resampling with perturbation of ...
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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 ...
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Automated identification of Monogeneans using digital image processing and K nearest neighbour approaches
... train and test the system. K-nearest neighbour was used for detection of four species of monogeneans according to their diagnostic parts. The accuracy of automated identifi- cation in this study is ...
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Discretisation of Data in a Binary Neural k-Nearest Neighbour Algorithm
... a k-Nearest Neighbour predictor. Our k-NN is constructed using binary neural networks which require continuous-valued data to be discretised to allow it to be mapped to the binary neural ...
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Storm Identification in the Rainfall Data Using Singular Value Decomposition and K-Nearest Neighbour Classification
... In this work, designed and implemented a model for relevance feature discovery. It discovers rainfall conditions as higher level features and deploys them over low-level features (terms) using singular value ...
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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 ...
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Improving classification performance of k nearest neighbour by hybrid clustering and feature selection for non communicable disease prediction
... the k-nearest neighbour is to determine the optimal size of k, which acts as a smoothing ...small k will not be sufficient to accurately estimate the population proportions around the ...
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Personalized Recommendation of Movies Using a Combined approach of locality sensitive hashing, K-Nearest neighbour and collaborative filtering.
... After registering into the system, user’s profiles are analysed and the user profile is generated. Generating the user profile is not focused in this paper. To have a better personalized recommendation, the demographic ...
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A binary neural k-nearest neighbour technique
... Abstract. K-Nearest Neighbour (k-NN) is a widely used technique for classifying and clustering ...data. k-NN is effective but is often criticised for its polynomial run-time growth as ...
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Improved AURA k-Nearest Neighbour approach
... orcid.org/0000-0001-5957-2474 et al. (2 more authors) (2003) Improved AURA k-Nearest Neighbour approach. In: Mira, J and Alvarez, JR, (eds.) ARTIFICIAL NEURAL NETS PROBLEM SOLVING METHODS, PT II. 7th ...
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Improving the Accuracy of K-Nearest Neighbour Method in Long Lead Hydrological Forecasting
... of K-Nearest Neighbour (K-NN) has been used in a variety of eco-hydrological ...the K-NN method in forecasting accumulated 9- month inow, from 1971 to 2001, of Zayandeh-rud dam in Iran, ...
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Abnormal Moving Object Detection Using Sparse Based Graph K Nearest Neighbour(SGk-NN)
... processing k-nearest neighbour queries is most important in moving object related ...of k-nearest neighbor Queries in centralized setting ...of K Nearest Neighbor Queries ...
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Introduction to k Nearest Neighbour Classification and Condensed Nearest Neighbour Data Reduction
... However, deciding whether two observations are similar or not is quite an open question. For instance, deciding whether two colours are similar is a completely different process to deciding whether two paragraphs of text ...
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k Nearest Neighbour Join using Map Reduce for effective Smart Travel Guide
... the nearest set of items or every one of the articles inside of a separation edge to every item in the ...the nearest neighbors, ands close neighbors, and can do as such as to a great degree substantial ...
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