Euclidean Distance (ED)
Structure method for solving the nearest Euclidean distance matrix problem
... 1. Al-Homidan, S, Fletcher, R: Hybrid methods for finding the nearest Euclidean distance matrix. In: Du, D, Qi, L, Womersley, R (eds.) Recent Advances in Nonsmooth Optimization, pp. 1-17. World Scientific, ...
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Penalised Euclidean distance regression
... penalty is the main contribution of the paper. The advantage of our approach is that we are able to provide a pivotal recovery property, and in addition gain the grouping property of the Elastic Net (where regression ...
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Handwritten Character Recognition Using Multiresolution Technique and Euclidean Distance Metric
... In the present paper, the problem of handwritten character recognition has been tackled with multiresolution technique using Discrete wavelet transform (DWT) and Euclidean distance metric (EDM). The ...
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Acculturation trajectories and quality of life in South African immigrants living in New Zealand : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Clinical Psychology at Massey University, Palmerston North, New Zealand
... squared Euclidean distance measures were used to capture how two cases differed in their profile on the variables (see appendices H-O for Binary squared Euclidean distance matrices for each ...
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Error Evaluation on K Means and Hierarchical Clustering with Effect of Distance Functions for Iris Dataset
... This simulation result chart show as two dimension xy plane in this figure 8: x- axis as a k-means clustering algorithm and y- axis show as a clustering error (in %) , the bold line show as a iris data set in this graph ...
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Face Recognition using Euclidean Distance Correlation Algorithm
... Biometric identification has attracted much attention in recent times for implementing it for security purpose and also for access, attendance etc. It also has great potential in numerous applications like in criminal ...
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Face Recognition using Euclidean Distance Correlation Algorithm
... Biometric identification has attracted much attention in recent times for implementing it for security purpose and also for access, attendance etc. It also has great potential in numerous applications like in criminal ...
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I/O MATCH (I/O MAT) AND BEHAVIORAL MATCH (BEH MAT) BASED SEMANTIC WEB SERVICE DISCOVERY
... problems. Euclidean distance is a well known and commonly used metric in most of the ...this distance metric include blind to correlated variables, not robust in noisy environment, affected by ...
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Multiwavelet based Texture Features for Content based Image Retrieval
... Euclidean distance is not always the best ...the distance between two ...Canberra distance metric, which motivates us to use Canberra distance metric as dissimilarity ...Canberra ...
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Extraction of Texture features Using Euclidean, Canberra and Both Distance
... Canberra distance metric for similarity measurement improves the retrieval performance from ...traditional Euclidean distance metric (where same features were used but Euclidean ...
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3 Level Techniques Comparison based Image Recognition
... with this, any one strongly believe that the pivot of image recognition should be arranged around learning processes at all levels of feature extraction and object recognition [11]. In this work, image recognition using ...
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Significant Sentence Extraction by Euclidean Distance Based on Singular Value Decomposition
... the Euclidean distances can be computed between all the sentence vectors and the significant term vec- tors (not all the term ...the distance is, the more important the sentence is, since the significant ...
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An analysis of Euclidean Distance preserving perturbation for Privacy Preserving Data Mining
... Recently, distance preserving data perturbation has gained attention because it mitigates the privacy/accuracy trade-off by guaranteeing perfect ...e.g., distance-based clustering and k-nearest neighbor ...
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A Standardized Treatment of Binary Similarity Measures with an Introduction to k Vector Percentage Normalized Similarity
... Manhattan distance between the two vectors and will be seen more in this ...Other distance measures can also be used here as well. Euclidean Distance can be used as it treats the differences ...
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2D Face Recognition Based on PCA & Comparison of Manhattan Distance, Euclidean Distance & Chebychev Distance
... that describes the contribution of each eigenface in representing the input face image, treating the eigenfaces as a basis set for face images. The vector may then be used in a standard pattern recognition algorithm to ...
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Effectively and Efficiency Consideration for Spatial Database
... using Euclidean Distance to compute the similarity between them, and retrieve the sets of object from database based on Euclidean distance that make this situation is happen and ...
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Effect of Dynamic Time Warping using different Distance Measures on Time Series Classification
... as Euclidean Distance to measure the similarity between the time ...series. Euclidean distance suffers when the two time series vary in time ...this distance measure provides good ...
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Distances, Hesitancy Degree and Flexible Querying via Neutrosophic Sets
... Euclidean distance of the neutrosophic set R i from the upper and lower horizon, respectively. These two numbers show how close is the record r i to the best and to the worst possible record, respectively. ...
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Comparative Study of Fuzzy k Nearest Neighbor and Fuzzy C means Algorithms
... data. Distance measures are the most useful techniques to compute such ...several distance measures, such as Euclidean (ED), Manhattan (MH), Cosine (COS), Mahalanobis, Hamming, and so on ...
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A Study on the Balanced Assignment of Allocating Large Group with Multiple Attributes into Subgroups
... nobis distance for the purpose of clustering and ...halanobis distance smaller than a certain ...a distance calculation which converts multivariate data into a single numerical ...Mahalanobis ...
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