Results 241 to 250 of about 9,997,446 (295)

DISTANCE-RATIO LEARNING FOR DATA VISUALIZATION

International Journal of Wavelets, Multiresolution and Information Processing, 2012
Most dimensionality reduction methods depend significantly on the distance measure used to compute distances between different examples. Therefore, a good distance metric is essential to many dimensionality reduction algorithms. In this paper, we present a new dimensionality reduction method for data visualization, called Distance-ratio Preserving ...
He, Guanghui, Shang, Zhaowei, Chen, Hengxin   +2 more
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Estimating Response Ratios from Continuous Outcome Data

Methodology and Computing in Applied Probability, 2014
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Gåsemyr, Jørund, Natvig, Bent, Tvete, Ingunn Fride   +2 more
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Generalized Odds Ratios for Ordinal Data

Biometrics, 1980
We consider properties of the ordinal measure of association defined by the ratio of the proportions of concordant and discordant pairs. For 2 x 2 cross-classification tables, the measure simplifies to the odds ratio. The generalized measure can be used to summarize the difference between two stochastically ordered distributions of an ordinal ...
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Data-Driven Optimization of Reward-Risk Ratio Measures

INFORMS Journal on Computing, 2021
We investigate a class of fractional distributionally robust optimization problems with uncertain probabilities. They consist in the maximization of ambiguous fractional functions representing reward-risk ratios and have a semi-infinite programming epigraphic formulation.
Ran Ji, Miguel A. Lejeune
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Odds ratio inference with dependent data

Biometrika, 1985
SUMMARY When data can be presented as a series of k 2 x 2 tables with cell counts (xi, ni-xi; yi, mi - yi), it is often assumed that xi and yi are binomially distributed. This paper deals with inference for the common odds ratio i/l when the binomial assumption is invalid.
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Incomplete data classification—Fisher Discriminant Ratios versus Welch Discriminant Ratios

Future Generation Computer Systems, 2020
Abstract This study focuses on incomplete data classification with the support of different partial discriminant analyses. When samples contain missing values, discriminant analyses such as Principal Component Analysis and Fisher Discriminant Analysis are inapplicable. Partial discriminant analyses that measure the importance of individual dimensions
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Quantifiable data mining using ratio rules

The VLDB Journal The International Journal on Very Large Data Bases, 2000
Association Rule Mining algorithms operate on a data matrix (e.g., customers $\times$ products) to derive association rules [AIS93b, SA96]. We propose a new paradigm, namely, Ratio Rules, which are quantifiable in that we can measure the “goodness” of a set of discovered rules.
Flip Korn, Alexandros Labrinidis, Yannis Kotidis, Christos Faloutsos   +3 more
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