k-nearest
Rate of Convergence of $k$-Nearest-Neighbor Classification Rule
... weighted nearest neighbor classification including non-weighted k-nearest-neighbor classification, Samworth (2012a,b), with further references, considered the case when X is bounded, D is ...
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Using the Mutual k Nearest Neighbor Graphs for Semi supervised Classification on Natural Language Data
... the k-nearest neighbor graphs have been the de facto standard method of graph construction, this paper advocates using the less well-known mutual k-nearest neigh- bor graphs for ...
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A Short Term Traffic Flow Forecasting Method Based on a Three Layer K Nearest Neighbor Non Parametric Regression Algorithm
... three-layer K-nearest neighbor non-parametric regression algorithm is ...in K- nearest neighbor non-parametric regression method, and the forecasting results were output us- ing the weighted ...
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Pruned fuzzy K nearest neighbor classifier for beat classification
... simple K-nearest neighbor (SKNN) classifier has been employed for the classi- fication of 6 types of beats (Paced Beats (PB), Atrial Premature Beat (APB), Premature Ventricular Con- traction (PVC), Normal ...
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Prediction Using Back Propagation and k-Nearest Neighbour (k-NN) Algorithm
... ABSTRCT: Prediction of Stock Prices is not only inquisitiveness but also the very challenging topic. This paper intension is predict stock prices for sample of some major companies using back propagation and ...
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Exact fuzzy k Nearest neighbor classification for big datasets
... Currently, there are contributions to manage big data prob- lems with the kNN algorithm. Focusing on classification problems, the method proposed in [10] is an approximate kNN algorithm consisted of two stages. First, it ...
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Probabilistic Classification from a K Nearest Neighbour Classifier
... Abstract K-nearest-neighbours is a simple classifier, and with increasing size of training set, the accuracy of its class predictions can be made asymptotic to the upper ...
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Location And Query Privacy In K Nearest Neighbour Queries
... the k nearest neighbors (kNN) queries where the mobile user queries the LBS provider on k closest ...the nearest k POIs by comparing the distances between the mobile user's location and ...
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Generalized Mean Distance-based k Nearest Centroid Neighbor Classifier
... Non-parametric classifiers provide simple and effective strategies in pattern classification. Especially, the conventional k nearest neighbor (kNN) (Stevens et al., 1967) has been widely used in the ...
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Simulation of Agricultural Logistic Processes with k-Nearest Neighbors Algorithm
... the k-Nearest Neighbors algorithm (kNN) is ...The k-Nearest Neighbors algorithm which has been determined during the examination, makes it possible, to estimate the key parameters energy and ...
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A Novel Weighted Voting for K-Nearest Neighbor Rule
... size k of many data sets is determined as one as shown in Table ...size k guarantees a lower error rate only if the distribution around a query object is dense enough, that is, there are a great many ...
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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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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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What Affects K Value Selection In K-Nearest Neighbor
... etc. k-Nearest Neighbor (k-NN) is a classifier that classify an object based on the majority vote of its ...the k value will determine the performance of the ...of k value. Different ...
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Evaluating the Performance of Dual Weighted K Nearest Neighbor Classifier
... A non-parametric, very simple to use, effective instance-based learning algorithm called K-Nearest Neighbor (KNN), is most widely used to classify the objects in data mining. KNN has some shortcomings which ...
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Classification Of Medical Image Data Using K Nearest Neighbor And Finding The Optimal K Value
... into k NN classifiers. Findings presented that the k NN degraded performance due to its inability of handling high dimensional ...using k NN is a tedious task due to higher mean squared ...using ...
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A Review of Data Classification Using K-Nearest Neighbour Algorithm
... for K-Near neighbor ...has k nearest examples where feature space and majority of them are having the same label “y”, then “x” belongs to ...The K-NN method is mostly depends upon furthermost ...
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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 K nearest neighbour measured by a distance ...
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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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k-Nearest Neighbors Queries in Time-Dependent Road Networks
... processing k-nearest neighbors (kNN) queries in road networks considering traffic conditions, in particular the case where the speed of moving objects is ...the k points of interest ...
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