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About 10,000 papers for “Distance measures”

Distance Measures

Claude Sammut, Geoffrey I. Webb · Encyclopedia of Machine Learning and Data Mining · 2017

Cited by 0

Distance measures.

Luis Payá (3993287), Oscar Reinoso (3993290), Luis M. Jiménez (3993296) et al. · Figshare · 2017

Cited by 0Open access

Distance Measures

Shashi Shekhar, Hui Xiong, Xun Zhou · Encyclopedia of GIS · 2017

Cited by 0

Running head: PHONOLOGICAL DISTANCE MEASURES Phonological Distance Measures

Nathan C Sanders, Steven B Chin, Phonological Distance Measures · 2008

Phonological distance measures 2 Phonological distance can be measured computationally using formally specified algorithms. This work investigates two such measures, one developed by Nerbonne and Heeringa (1997) based on Levenshtein distance (Levenshtein, 1965) and the other an adaptation of Dunning’s (1994) language classifier that uses maximum likelihood distance. These two measures are compared against naïve transcriptions of the speech of pediatric cochlear implant users. The new measure, maximum likelihood distance, correlates highly with Levenshtein distance and naïve transcriptions; results from this corpus are easier to obtain since cochlear implant speech has a lower intelligiblity than the usually high intelligibility of the speech of a different dialect. Phonological Distance Measures Phonological distance measures 3 Measuring linguistic distance has proceeded in a number of ways over the last two centuries. Early methods were only applicable to a specific area; in dialectology, for

Cited by 0

Phonological Distance Measures*

Nathan C. Sanders, Steven B. Chin · Journal of Quantitative Linguistics · 2009

Phonological distance can be measured computationally using formally specified algorithms. This work investigates two such measures, one developed by Nerbonne and Heeringa (1997) based on Levenshtein distance (Levenshtein, 1965) and the other an adaptation of Dunning's (1994) language classifier that uses maximum likelihood distance. These two measures are compared against naïve transcriptions of the speech of pediatric cochlear implant users. The new measure, maximum likelihood distance, correlates highly with Levenshtein distance and naïve transcriptions; results from this corpus are easier to obtain since cochlear implant speech has a lower intelligibility than the usually high intelligibility of the speech of a different dialect.

Cited by 46Open access

Measures of Distance

Francesca Gallo · IRIS Research product catalog (Sapienza University of Rome) · 2011

Cited by 1

On Hausdorff Distance Measures

Shapiro, Matthew B. Blaschko · 2004

A number of Hausdorff-based algorithms have been proposed for finding objects in images. We evaluate different measures and argue that the Hausdorff Average distance measure outperforms other variants for model detection. This method has improved robustness properties with respect to noise. We discuss the algorithms with respect to typical classes of noise, and we illustrate their relative performances through an example edge-based matching task. We show that this method produces a maximum a posteriori estimate. Furthermore, we argue for improved computational efficiency by tree-like subdivisions of the model and transformation spaces.

Cited by 29

Distance measures in bioinformatics

Feiyu Xiong, Feiyu Xiong, Leonid Hrebien · 2015

Many bioinformatics applications rely on the computation of similarities between objects. Distance and similarity measures applied to vectors of characteristics are essential to problems such as classification, clustering and information retrieval. This study explores the usefulness of distance and similarity measures in several bioinformatics applications. These applications are in two categories. (1) Estimation of the adverse reaction severity of unknown pharmaceutical treatments, based on the severity of known treatments, in order to provide guidance for testing of the unknown treatments in clinical trials. (2) Classification of cancer tissue types and estimation of cancer stages, based on high-dimensional microarray data, in order to support clinical decisions making. To address the first category, we studied several clustering and classification approaches for binary severity estimation of Cytokine Release Syndrome (CRS). We developed a Severity Estimation using Distance Metric Learning (SE-DML) approach to get graded severity estimation. With binary estimation we were able to identify treatments that caused the most severe response and then built prediction models for CRS. Using the SE-DML approach, we evaluated four known data sets and showed that SE-DML outperformed other widely used methods on these data sets. For the second category, we presented Kernelized Information-Theoretic Metric Learning (KITML) algorithms that optimize distance metrics and effectively handle high-dimensional data. This learned metric by KITML is used to improve the performance of k-nearest neighbor classification for cancer tissue microarray data. We evaluated our approach on fourteen (14) cancer microarray data sets and compared our results with other state-of-the-art approaches. We achieved the best overall performance for the classification task. In addition we tested the KITML algorithm in estimating the severity stages of cancer samples, with accurate results.

Cited by 0Open access

Distance Function Efficiency Measures

Chiang Kao · International series in management science/operations research/International series in operations research & management science · 2023

Cited by 0

Genetic Distance Measures: Review

İsmet DOĞAN, Nurhan DOĞAN · Turkiye Klinikleri Journal of Biostatistics · 2016

The major unsolved problem of descriptive population genetics is an adequate specification of the genetic difference between two closely related species as compared to the genetic difference between two populations of the same species. Traditional population genetic analyses deal with the distribution of allele frequencies between and within populations. From these frequencies several measures of population structure can be estimated, one of the most widely used being the genetic distance measures. Genetic distance is the degree of gene difference (genomic difference) between species or populations that is measured by some numerical method. Genetic distance measures have already been established as one of the major tools for analyzing data on gene differentiation between populations. Many genetic distances have been developed, of which a few remain in regular use. Each of these genetic distances has unique evolutionary and statistical properties, and evolutionary relationships inferred from each genetic distance can be quite different. Quantification of the genetic distance between populations is instrumental in many genetic research initiatives, and a large number of formulas for this purpose have been proposed. However, selection of an appropriate measure for assessing genetic distance between real-world human populations that diverged as a result of mechanisms that are not fully known can be a challenging task. In this study twenty six distance measures were investigated. For macroevolutionary comparisons, Nei's measures are probably the best. In microevolutionary studies, when sample sizes are approximately equal and the differences in gene frequency are great, Edward's E 2 is preferable. If sample sizes are quite variable and gene frequencies do not differ greatly, Sanghvi's G2 would be most appropriate.

Cited by 27Open access

Evaluating linguistic distance measures

Wichmann, S., Holman, E., Bakker, D. et al. · RePEc: Research Papers in Economics · 2010

In Ref. [13], Petroni and Serva discuss the use of Levenshtein distances (LD) between words referring to the same concepts as a tool for establishing overall distances among languages which can then subsequently be used to derive phylogenies. The authors modify the raw LD by dividing the LD by the length of the longer of the two words compared, to produce what could be called LDN (normalized LD). Other scholars [7, 8] have used a further modification, where they divide the LDN by the average LDN among words not referring to the same concept. This produces what could be called LDND. The authors of Ref. [13] question whether LDND is a more adequate measure of distance than LDN. Here we show empirically that LDND is the better measure in the situation where the languages compared have not already been shown, by other, more traditional methods of comparative linguistics, to be related. If automated language classification is to be used as a tool independent of traditional methods then the further modification is necessary.

Cited by 0

Old measures of distance

C. Roy Hudleston · Notes and Queries · 1934

Journal Article Old measures of distance Get access C. Roy Hudleston C. Roy Hudleston 1The Grove, Winterbourne Search for other works by this author on: Oxford Academic Google Scholar Notes and Queries, Volume CLXVII, Issue dec01, 1 December 1934, Page 390, https://doi.org/10.1093/nq/CLXVII.dec01.390d Published: 01 December 1934

Cited by 0

The employed distance measures.

Karl G. Kugler (279206), Laurin A. J. Mueller (279211), Armin Graber (279218) et al. · Figshare · 2015

<p>Here, we list the 5 distance measures that were used for the selection of a graph prototype.</p>

Cited by 0Open access