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Robust Distance Measures for NN Classification of Cancer Data [PDF]
Cancer Informatics, 2020 The k -Nearest Neighbor ( k NN) classifier represents a simple and very general approach to classification. Still, the performance of k NN classifiers can often compete with more complex machine-learning algorithms.
Rezvan Ehsani, Finn Drabløs
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Quantum Distance Measures Based upon Classical Symmetric Csiszár Divergences [PDF]
Entropy, 2023 We introduce a new family of quantum distances based on symmetric Csiszár divergences, a class of distinguishability measures that encompass the main dissimilarity measures between probability distributions.
Diego G. Bussandri, Tristán M. Osán
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Phonological Distance Measures. [PDF]
J Quant Linguist, 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
Sanders NC, Chin SB.
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DISTANCE MEASURES IN AGGREGATING PREFERENCE DATA [PDF]
Acta Universitatis Lodziensis. Folia Oeconomica, 2014 The aim of this paper is to present aggregation methods of individual preferences scores by means of distance measures. Three groups of distance measures are discussed: measures which use preference distributions for all pairs of objects (e.g.
Artur Zaborski
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Interrelations of graph distance measures based on topological indices. [PDF]
PLoS ONE, 2014 In this paper, we derive interrelations of graph distance measures by means of inequalities. For this investigation we are using graph distance measures based on topological indices that have not been studied in this context.
Matthias Dehmer +2 more
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On distance, similarity and entropy measures of multidimensional fuzzy sets [PDF]
Scientific ReportsThis article examines distance and similarity measures in multidimensional fuzzy sets, which are essential in decision-making and aggregation across various fields.
Jomal Josen +2 more
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Norms and Delta-Equalities of Complex Neutrosophic Sets [PDF]
Neutrosophic Sets and Systems, 2022 The purpose of this paper is to put forward the basics results of complex fuzzy sets (CFSs) such as union, intersection, complement, product into complex neutrosophic sets because as the CFSs and complex intuitionistics sets does give the erroneous and ...
Madad Khan +4 more
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On Distance-Based Attribute Reduction With
IEEE Access, 2023 Attribute reduction, often referred to as feature selection, is a vital step in data preprocessing aimed at eliminating unnecessary attributes and enhancing the efficiency of classification models.
Pham Viet Anh +3 more
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Biology, 2023 Predictive models based on empirical similarity are instrumental in biology and data science, where the premise is to measure the likeness of one observation with others in the same dataset.
Jeniffer D. Sanchez +5 more
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Quantitative Methods for Analyzing Second Language Lexical Tone Production
Languages, 2023 The production of L2 lexical tone has proven difficult for learners of tonal languages, leading to the testing of different tone training techniques. To test the validity of these techniques, it is first necessary to capture the differences between L1 ...
Alexis Zhou, Daniel J. Olson
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