Results 261 to 270 of about 357,766 (300)

Trimmed fuzzy clustering for interval-valued data

Advances in Data Analysis and Classification, 2014
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
D'URSO, Pierpaolo, Livia De Giovanni, MASSARI, Riccardo   +2 more
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Multiview Classification Through Learning From Interval-Valued Data

IEEE Transactions on Neural Networks and Learning Systems
The classification problem concerning crisp-valued data has been well resolved. However, interval-valued data, where all of the observations' features are described by intervals, are also a common data type in real-world scenarios. For example, the data extracted by many measuring devices are not exact numbers but intervals.
Guangzhi Ma, Jie Lu, Zhen Fang, Feng Liu, Guangquan Zhang   +4 more
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Multiple mediation analysis for interval-valued data

Statistical Papers, 2017
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Calcagnì, Antonio, Lombardi, Luigi, Avanzi, Lorenzo, Pascali, Eduardo   +3 more
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The Sign Test for Interval-Valued Data

2016
Two versions of the generalized sign test for interval-valued data are proposed. Each version correspond to a different view on the interval outcomes of the experiment—either the epistemic or the ontic one. As it is shown, each view yield different approaches to data analysis and statistical inference.
Przemysław Grzegorzewski, Martyna Śpiewak   +1 more
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Minimal Learning Machine for Interval-Valued Data

2018 7th Brazilian Conference on Intelligent Systems (BRACIS), 2018
Solving regression problems with interval-valued datasets is a challenging task that may arise in many real world applications. Motivated by that fact, many researchers have proposed nonlinear regression methods to handle interval-valued data in recent years.
Diego Farias de Oliveira, Nykolas Mayko Maia Barbosa, Alisson Sampaio Carvalho de Alencar, Joao Paulo Pordeus Gomes, Leonardo Ramos Rodrigues   +4 more
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Monotonic Decision Tree for Interval Valued Data

2014
Traditional decision tree algorithms for interval valued data only can deal with non-ordinal classification problems. In this paper, we presented an algorithm to solve the ordinal classification problems, where both the condition attributes with interval values and the decision attributes meet the monotonic requirement.
Hong Zhu, Junhai Zhai, Shanshan Wang, Xizhao Wang   +3 more
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Multidimensional scaling of interval-valued dissimilarity data

Pattern Recognition Letters, 2000
Multidimensional scaling is a well-known technique for representing measurements of dissimilarity among objects as points in a p-dimensional space. In this paper, this method is extended to the case where dissimilarities are only known to lie within certain intervals.
T. Denœux, M. Masson
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Nonlinear regression applied to interval-valued data

Pattern Analysis and Applications, 2016
This paper introduces a nonlinear regression model to interval-valued data. The method extends the classical nonlinear regression model in order to manage interval-valued datasets. The parameter estimates of the nonlinear model considers some optimization algorithms aiming to identify which one presents the best accuracy and precision in the prediction
Eufrásio de A. Lima Neto, Francisco de A. T. de Carvalho   +1 more
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Hierarchical Clustering for Interval-Valued Functional Data

2011
In this paper, we deal with hierarchical clustering for interval-valued functional data. Functional data is defined as the data which is function, or as the data approximated as a function. Functional cluster analysis is proposed as cluster analysis for functional data.
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LINEAR REGRESSION ANALYSIS FOR INTERVAL-VALUED DATA

Advances and Applications in Statistics
This study deals with regression analysis using data where the observations are given as interval values. Regression analysis of interval-valued data aims to estimate the upper and lower boundaries of the interval of the target variable for a given interval of the explanatory variable.
Ryo Mizushima, Asanao Shimokawa
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