Relevant bibliographies by topics / Nominal distance

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  1. Journal articles

Academic literature on the topic 'Nominal distance'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Nominal distance"

1

Williamson, Craig A., and Leon N. McLin. "Nominal ocular dazzle distance (NODD)." Applied Optics 54, no. 7 (2015): 1564. http://dx.doi.org/10.1364/ao.54.001564.

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2

Mlynczak, Jaroslaw, Krzysztof Kopczynski, Miron Kaliszewski, and Maksymilian Wlodarski. "Estimation of nominal ocular hazard distance and nominal ocular dazzle distance for multibeam laser radiation." Applied Optics 60, no. 22 (2021): 6414. http://dx.doi.org/10.1364/ao.431490.

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3

Wilson, D. R., and T. R. Martinez. "Improved Heterogeneous Distance Functions." Journal of Artificial Intelligence Research 6 (January 1, 1997): 1–34. http://dx.doi.org/10.1613/jair.346.

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Instance-based learning techniques typically handle continuous and linear input values well, but often do not handle nominal input attributes appropriately. The Value Difference Metric (VDM) was designed to find reasonable distance values between nominal attribute values, but it largely ignores continuous attributes, requiring discretization to map continuous values into nominal values. This paper proposes three new heterogeneous distance functions, called the Heterogeneous Value Difference Metric (HVDM), the Interpolated Value Difference Metric (IVDM), and the Windowed Value Difference Metric
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4

Mlynczak, Jaroslaw, Krzysztof Kopczynski, Miron Kaliszewski, and Maksymilian Wlodarski. "Estimation of nominal ocular hazard distance and nominal ocular dazzle distance for multibeam laser radiation: publisher’s note." Applied Optics 60, no. 23 (2021): 6849. http://dx.doi.org/10.1364/ao.438712.

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5

El Hindi, Khalil. "Specific-class distance measures for nominal attributes." AI Communications 26, no. 3 (2013): 261–79. http://dx.doi.org/10.3233/aic-130565.

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6

Kurtz, Camille, Pierre Gançarski, Nicolas Passat, and Anne Puissant. "A hierarchical semantic-based distance for nominal histogram comparison." Data & Knowledge Engineering 87 (September 2013): 206–25. http://dx.doi.org/10.1016/j.datak.2013.06.002.

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7

Rosario, Geraldine E., Elke A. Rundensteiner, David C. Brown, Matthew O. Ward, and Shiping Huang. "Mapping Nominal Values to Numbers for Effective Visualization." Information Visualization 3, no. 2 (2004): 80–95. http://dx.doi.org/10.1057/palgrave.ivs.9500072.

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Data sets with a large numbers of nominal variables, including some with large number of distinct values, are becoming increasingly common and need to be explored. Unfortunately, most existing visual exploration tools are designed to handle numeric variables only. When importing data sets with nominal values into such visualization tools, most solutions to date are rather simplistic. Often, techniques that map nominal values to numbers do not assign order or spacing among the values in a manner that conveys semantic relationships. Moreover, displays designed for nominal variables usually canno
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8

Diab, Diab M., and Khalil El Hindi. "Using differential evolution for improving distance measures of nominal values." Applied Soft Computing 64 (March 2018): 14–34. http://dx.doi.org/10.1016/j.asoc.2017.12.007.

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9

Gong, Fang, Liangxiao Jiang, Dianhong Wang, and Xingfeng Guo. "Averaged one-dependence inverted specific-class distance measure for nominal attributes." Journal of Experimental & Theoretical Artificial Intelligence 32, no. 4 (2019): 651–63. http://dx.doi.org/10.1080/0952813x.2019.1661587.

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10

Gong, Fang, Liangxiao Jiang, Huan Zhang, Dianhong Wang, and Xingfeng Guo. "Gain ratio weighted inverted specific-class distance measure for nominal attributes." International Journal of Machine Learning and Cybernetics 11, no. 10 (2020): 2237–46. http://dx.doi.org/10.1007/s13042-020-01112-8.

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