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  • Search: subject:"Massive data"
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Year of publication
Subject
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Massive data 2 Artificial intelligence 1 Classification 1 Computational time efficiency 1 Coresets 1 D-optimality 1 Klassifikation 1 Künstliche Intelligenz 1 Mustererkennung 1 Pattern recognition 1 Polynomial regression 1 Science 1 Spherical model 1 Statistics and Numeric Data 1 Subdata 1 Support Vector Machine (SVM) 1 Theorie 1 Theory 1 Weak SVMs 1 composite likelihood 1 estimating equations 1 estimation efficiency 1 massive data 1 spatial-clustered 1 spatio-temporal 1
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Online availability
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Free 3 CC license 1
Type of publication
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Article 2 Other 1
Type of publication (narrower categories)
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Article 1 Article in journal 1 Aufsatz in Zeitschrift 1
Language
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English 3
Author
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Ara, Anderson 1 Bai, Yun 1 Ospina, Raydonal 1 Pimentel, Jonatha Sousa 1 Reuter, Torsten 1 Schwabe, Rainer 1
Published in...
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Decision analytics journal 1 Statistical Papers 1
Source
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BASE 1 ECONIS (ZBW) 1 EconStor 1
Showing 1 - 3 of 3
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A novel fusion Support Vector Machine integrating weak and sphere models for classification challenges with massive data
Pimentel, Jonatha Sousa; Ospina, Raydonal; Ara, Anderson - In: Decision analytics journal 11 (2024), pp. 1-14
The unprecedented growth in data generation has necessitated the adoption of advanced analytical techniques. Support Vector Machine (SVM) is a powerful machine learning tool that has proven invaluable in classifying observations through optimal hyperplane in higher dimensions. Despite their...
Persistent link: https://www.econbiz.de/10015101975
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Optimal subsampling design for polynomial regression in one covariate
Reuter, Torsten; Schwabe, Rainer - In: Statistical Papers 64 (2023) 4, pp. 1095-1117
Improvements in technology lead to increasing availability of large data sets which makes the need for data reduction and informative subsamples ever more important. In this paper we construct D -optimal subsampling designs for polynomial regression in one covariate for invariant distributions...
Persistent link: https://www.econbiz.de/10015193587
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Joint Composite Estimating Functions in Spatial and Spatio-Temporal Models.
Bai, Yun - 2011
Spatial or spatio-temporal data are frequently encountered in many scientific disciplines. One major challenge in modeling these processes is the high dimensionality of such data; that is, the number of observations is usually enormous. The first part of the dissertation proposes an efficient...
Persistent link: https://www.econbiz.de/10009482959
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