A graphical heuristic for reduction and partitioning of large datasets for scalable supervised training

A graphical heuristic for reduction and partitioning of large datasets for scalable supervised training

Abstract A scalable graphical method is presented for selecting and partitioning datasets for the training phase of a classification task. For the heuristic, a clustering algorithm is required to get its computation cost in a reasonable proportion to the task itself. This step is succeeded by constr...

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Bibliographic Details
Main Authors: Sumedh Yadav, Mathis Bode
Format: Article
Language:English
Published: SpringerOpen 2019-10-01
Series:Journal of Big Data
Subjects:
Training set selection/reduction
Distributed machine learning
Classification
Training set partition
Network architecture design
Data variety
Online Access:http://link.springer.com/article/10.1186/s40537-019-0259-3

Internet

http://link.springer.com/article/10.1186/s40537-019-0259-3

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