K-nearest neighbour method
Improving the Accuracy of K-Nearest Neighbour Method in Long Lead Hydrological Forecasting
... regression method 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 ...
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An Effective Candidate Refinement Approach For High Dimensional Of K-Nearest Neighbour Search
... our method on several big-scale datasets, and display that it permits correct and rapid overall performance for example-based totally item type, characteristic matching, and content-primarily based ...
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Location And Query Privacy In K Nearest Neighbour Queries
... server in D tuples and / or user requests. A server may be curious either because it is simply curious or because it has been compromised to become curious in the name of a third party without its explicit knowledge. In ...
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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
... new method for estimating the mean demand and a smoothing method for estimating the variance of the forecasted demand ...the method of forecasting mean demand produces biased ...
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Automated identification of Monogeneans using digital image processing and K nearest neighbour approaches
... Automated identification of monogenean species based on haptoral organ images of monogeneans achieved an overall accuracy of 90%. Image processing techniques were applied to automatically extract features from microscope ...
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A Review on Plant Texture Analysis Using Leaf Images
... the nearest neighbour (1NN) and kNN classifiers, it can be found that the MMC classifier can not only save the storage space but also reduce the classification ...proposed method is more robust ...
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Towards Detecting Deception using K Nearest Neighbour Model
... specific behaviours ([3]; [4]; [5]; [6]). In other words, there is nothing like Pinocchio’s growing nose. However, liars might experience emotions while lying and the three most common types of emotion associated with ...
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A COST SENSITIVE LEARNING METHOD TO TUNE THE NEAREST NEIGHBOUR FOR INTRUSION DETECTION
... adaptive K-NN classification algorithm is proposed that is based on the concept of statistical confidence from hypotheses ...This method takes into account the effective influence size of each training ...
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Discretisation of Data in a Binary Neural k-Nearest Neighbour Algorithm
... binning method is favoured by the data ...AURA k-NN in turn to predict the t+ 1 attribute value for the selected prediction attribute for that data ...10 nearest neighbours to generate the t+ 1 ...
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A Review of Data Classification Using K-Nearest Neighbour Algorithm
... The resulting similarity ranges from −1 meaning exactly opposite, to 1 meaning exactly the same, with 0 usually indicating independence, and in-between values indicating intermediate similarity or dissimilarity. For text ...
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Freeman chain code as representation in offline signature verification system
... parameters. Nearest k-Neighbour (k-NN) classifier performs matching score calculation based on Euclidean distance ...The method to obtain EER can be referred in ...
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Scalable Distributed Processing of K nearest Neighbour Queries over Moving Objects
... distributed k-NN search algorithm, to address this ...the k-NNs for a given query with only two ...proposed method. We would like to explore how to evaluate continuous k-NN queries over moving ...
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Probabilistic Classification from a K Nearest Neighbour Classifier
... Figure 3. The proposed novel density estimator framework using tabulated statistics calculated from an internal data-set. A posterior estimate for the unseen query sample can then be calculated using the prior estimates. ...
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A binary neural k-nearest neighbour technique
... The method in this paper is 1.8 times faster than the AURA k-NN detailed in (Weeks et ...the method in this paper. The recall accuracy for the AURA k-NN is 97% for the REAL data set and 99% ...
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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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Heart Disease Prediction System Using K Nearest Neighbour Classification Technique
... In [7], Tahmida Tabassum, discussed a work where Electrocardiogram (ECG) gives useful information about morphological details of heart which is used to find various cardiac diseases. It deals with a method of ...
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Improving classification performance of k nearest neighbour by hybrid clustering and feature selection for non communicable disease prediction
... Non-communicable Diseases (NCDs) are leading mortality rate and cause of death in worldwide. NCDs also known as chronic diseases are a long-lasting condition that can be controlled, but not cured. Top three main types of ...
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Experience with Rule Induction and k Nearest Neighbour Methods for Interface Agents that Learn
... When the user next reads news, they can choose one of two modes: agent mode or browse mode. When in browse mode, there is no agent intervention in the presentation of articles to the user; all articles are presented, ...
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A Study on Content Based Image Retrieval System using K-NN Algorithm and Mobile Agents
... that K-NN keeps all the training ...especially K-NN – make decision based on the entire training data set (in the best case a subset of ...the k-nearest neighbour algorithm ...
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