k-nearest neighbour methods
Experience with Rule Induction and k Nearest Neighbour Methods for Interface Agents that Learn
... Few studies have previously been conducted which compare CN2 with instance-based algorithms that utilize the value-distance metric. PEBLS [18] is an instance-based algorithm which uses a modified version of this distance ...
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Discretisation of Data in a Binary Neural k-Nearest Neighbour Algorithm
... discretisation methods are able to autonomously determine the number of partitions but this number is frequently user-specified using either a heuristic or by evaluating a range of values using a suitable ...
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Optimizing automatic morphological classification of galaxies with machine learning and deep learning using Dark Energy Survey imaging
... We show that the machine learning algorithms, Logistic Re- gression (LR) and Support Vector Machine (SVM) improve the performance of machine learning when combining with neural net- works features, such as Restricted ...
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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 ...favourite methods for measuring the distance between ...
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Heart Disease Prediction System Using K Nearest Neighbour Classification Technique
... The heart is an operating system of the human body. If it does not function properly it will affect other parts also. Heart disease problem describes a range of conditions that affect the heart. The existing system uses ...
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Density Estimation methods based on Mass
... estimation methods such as kernel density estimator and k-nearest neighbour density estimator have high time and space complexities which render them difficult to apply in problems with large ...
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A Novel Approach to Fuzzy-Based Facial Feature Extraction and Face Recognition
... Abstract: Generalized two-dimensional Fisher’s linear discriminant (G-2DFLD) is an effective feature extraction technique that maximizes class separability along row and column directions simultaneously. In this paper, ...
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Density Estimation methods based on Mass
... estimation methods such as kernel density estimator and k-nearest neighbour density estimator have high time and space complexities which render them difficult to apply in problems with large ...
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Automatic interpretation of nucleoside mass spectra
... The pattern recognition methods of statistical linear discriminant function analysis, learning machine approach, distance from the mean, and k-nearest neighbour classification have been [r] ...
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Improving the Accuracy of K-Nearest Neighbour Method in Long Lead Hydrological Forecasting
... by K- NN, Araghinejad and Burn [12] showed that the use of a combination of K-NN and geostatistical methods in hydrological forecasting can lead to more reliable results in long-term management of ...
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ANNF Estimation Technique and Methods for Finding nearest Neighbour Field
... these methods were developed for d-dimensional vectors, and do not take into consideration any image ...conventional k-nearest neighbor algorithms can be used effectively on these feature ...
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Sales Forecasting using Linear Regression and K-Nearest Neighbour
... Teunter, Ruud & Sani, Babangida [4] presented a predictive error-forecasting model which compares and evaluates forecasting methods based on their factor levels when faced with intermittent demand. The ...
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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 ...grid-based methods, which require an uncertain number of ...continuous k-NN queries over ...
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An Effective Candidate Refinement Approach For High Dimensional Of K-Nearest Neighbour Search
... retrieval methods are important for large-scale and information-pushed imaginative and prescient ...existing methods do no longer follow for high-dimensional statistics when the underlying characteristic ...
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Consensus speed optimisation with finite leadership perturbation in k-nearest neighbour networks
... 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 effectively identify ...
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A Review of Data Classification Using K-Nearest Neighbour Algorithm
... Data mining is the extraction of veiled information from large database. Classification is a data mining task of forecasting the value of a categorical variable by building a model based on one or more numerical and/or ...
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Location And Query Privacy In K Nearest Neighbour Queries
... This article proposes hybrid techniques for approximate and accurate private NN queries that provide protection to users and the service provider. Our solutions rely on cryptographic protocols for the private evaluation ...
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Prediction Using Back Propagation and k-Nearest Neighbour (k-NN) Algorithm
... Many investors or researchers claim that the stock market is a chaos system. Chaos is a non linear deterministic system which only appears arbitrary because of its asymmetrical fluctuations. Investors, business ...
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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 ...
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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 ...of methods using this methodology regularly relies on upon the attributes of the items and the join ...
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