Text dependent Speaker Recognition using LPC_avg and Euclidean distance measure

Self-adapt distance measure supervised locally linear embedding solves the problem that Euclidean distance measure can not apart from samples in content-based image retrieval. This method uses discriminative distance measure to construct k-NN and effectively keeps its topological structure in high dimension space, meanwhile it broadens interval of samples and strengthens the ability of classifying. Experiment results show the ADM-SLLE date-reducing-dimension method speeds up the image retrieval and acquires high accurate rate in retrieval.

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A new decision based unsymmetric trimmed median filter using Euclidean distance measure for removal of high density Salt and Pepper noise from images

International Conference on Information Communication and Embedded Systems (ICICES2014) ◽

10.1109/icices.2014.7033825 ◽

2014 ◽

Cited By ~ 1

Author(s):

T. Santhanam ◽

K. Chithra

Keyword(s):

Euclidean Distance ◽

Distance Measure ◽

Median Filter ◽

High Density ◽

Salt And Pepper Noise ◽

Euclidean Distance Measure ◽

Salt And Pepper

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Similarity measures of Pythagorean fuzzy sets based on combination of cosine similarity measure and Euclidean distance measure

10.1063/1.5041661 ◽

2018 ◽

Cited By ~ 5

Author(s):

Wan Rosanisah Wan Mohd ◽

Lazim Abdullah

Keyword(s):

Fuzzy Sets ◽

Similarity Measure ◽

Euclidean Distance ◽

Distance Measure ◽

Similarity Measures ◽

Cosine Similarity ◽

Pythagorean Fuzzy Sets ◽

Euclidean Distance Measure ◽

Cosine Similarity Measure

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A convolution-based distance measure for fuzzy singletons and its application in a pattern recognition problem

Integrated Computer-Aided Engineering ◽

10.3233/ica-200629 ◽

2020 ◽

Vol 28 (1) ◽

pp. 51-63 ◽

Cited By ~ 2

Author(s):

Rodrigo Naranjo ◽

Matilde Santos ◽

Luis Garmendia

Keyword(s):

Pattern Recognition ◽

Fuzzy Number ◽

Euclidean Distance ◽

Distance Measure ◽

Distance Measures ◽

Trading System ◽

Weighted Distance ◽

Pattern Recognition Problem ◽

Euclidean Distance Measure ◽

Desirable Behavior

A new method to measure the distance between fuzzy singletons (FSNs) is presented. It first fuzzifies a crisp number to a generalized trapezoidal fuzzy number (GTFN) using the Mamdani fuzzification method. It then treats an FSN as an impulse signal and transforms the FSN into a new GTFN by convoluting it with the original GTFN. In so doing, an existing distance measure for GTFNs can be used to measure distance between FSNs. It is shown that the new measure offers a desirable behavior over the Euclidean and weighted distance measures in the following sense: Under the new measure, the distance between two FSNs is larger when they are in different GTFNs, and smaller when they are in the same GTFN. The advantage of the new measure is demonstrated on a fuzzy forecasting trading system over two different real stock markets, which provides better predictions with larger profits than those obtained using the Euclidean distance measure for the same system.

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Biases with the Generalized Euclidean Distance measure in disparity analyses with high levels of missing data

Palaeontology ◽

10.1111/pala.12430 ◽

2019 ◽

Vol 62 (5) ◽

pp. 837-849 ◽

Cited By ~ 5

Author(s):

Oscar E. R. Lehmann ◽

Martín D. Ezcurra ◽

Richard J. Butler ◽

Graeme T. Lloyd

Keyword(s):

Missing Data ◽

Euclidean Distance ◽

Distance Measure ◽

Euclidean Distance Measure

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Clustering of OECD Countries Out of Pocket Health Expenditure Time Series Data

Research in Applied Economics ◽

10.5296/rae.v8i2.9377 ◽

2016 ◽

Vol 8 (2) ◽

pp. 23

Author(s):

Songul Cinaroglu

Keyword(s):

Euclidean Distance ◽

Time Series Data ◽

Distance Measure ◽

Health Expenditures ◽

Oecd Countries ◽

Distance Measures ◽

Series Data ◽

Longest Common Subsequences ◽

Study Results ◽

Euclidean Distance Measure

Out of pocket health expenditures points out to the payments made by households at the point they receive health services. Frequently these include doctor consultation fees, purchase of medication and hospital bills. In this study hierarchical clustering method was used for classification of 34 countries which are members of OECD (Organization for Economic Cooperation and Development) in terms of out of pocket health expenditures for the years between 1995-2011. Longest common subsequences (LCS), correlation coefficient and Euclidean distance measure was used as a measure of similarity and distance in hierarchical clustering. At the end of the analysis it was found that LCS and Euclidean distance measures were the best for determining clusters. Furthermore, study results led to understand grouping of OECD countries according to health expenditures.

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Research on Optimal Strategy of Residential Buildings Energy Based on Standardized Euclidean Distance Measure Similarity Search Method

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/651/2/022052 ◽

2021 ◽

Vol 651 (2) ◽

pp. 022052

Author(s):

Yifeng Ding ◽

Anqi Liang ◽

Kai Ma ◽

Gan Li ◽

Zhao Wang ◽

...

Keyword(s):

Optimal Strategy ◽

Similarity Search ◽

Euclidean Distance ◽

Distance Measure ◽

Residential Buildings ◽

Search Method ◽

Euclidean Distance Measure

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A Comparison of Learning and Teaching Styles - Self-Perception of IT Students

10.28945/2826 ◽

2004 ◽

Author(s):

Zlatko J Kovacic

Keyword(s):

Learning Styles ◽

Learning Style ◽

Euclidean Distance ◽

Distance Measure ◽

Teaching Styles ◽

Learning Preferences ◽

The Self ◽

Self Perception ◽

Learning And Teaching ◽

Euclidean Distance Measure

This paper presents the results of an empirical study of the learning styles of a group of computing students and the teaching styles of their tutors at The Open Polytechnic of New Zealand. This study of learning styles is based on Kolb’s learning model and the Felder-Soloman learning style instrument. To identify how close students’ learning styles match the teaching styles of their tutors we have used two indicators: the self-perception of the students about the proximity of their learning styles and the teaching styles of their tutors and a Euclidean distance measure between students’ and tutors’ learning preferences. Using survey data and the learning styles instrument results we identify the differences between the learning and teaching styles. Both indicators show consistent and significant differences between the learning and teaching styles, in the way students and their tutors perceive and understand information. Finally we make recommendation to tutors on how to bridge this gap and address the learning styles of their students.

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Approximation Methods in Discrete Spatial Density Analysis for Finite Populations

Perceptual and Motor Skills ◽

10.2466/pms.1992.74.3.867 ◽

1992 ◽

Vol 74 (3) ◽

pp. 867-873 ◽

Cited By ~ 7

Author(s):

Frank O'Brien

Keyword(s):

Euclidean Distance ◽

Distance Measure ◽

Spatial Density ◽

Human Populations ◽

Finite Sample ◽

Root Model ◽

Euclidean Distance Measure ◽

Finite Sample Size ◽

Density Analysis

The author's three-parameter square-root model for the measurement of discrete spatial density in human populations was previously derived under the assumption that exact coordinate locations of the density points were available. The model, called the population density index (PDI) model, has been expanded to include a set of routines for calculating two-dimensional spatial density measures based upon in situ geometric approximations of the interobject Euclidean distance measure for any finite sample size. The derivation and specification of the algorithm for the abbreviated calculation routines are presented and exemplified. The author has been able to apply the methods of the PDI model to submarine environments at the U.S. Naval Underwater Systems Center, resulting in several U.S. Patent applications.

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