Development of Supervised Learning Predictive Models for Highly Non-linear Biological, Biomedical, and General Datasets

Development of Supervised Learning Predictive Models for Highly Non-linear Biological, Biomedical, and General Datasets

In highly non-linear datasets, attributes or features do not allow readily finding visual patterns for identifying common underlying behaviors. Therefore, it is not possible to achieve classification or regression using linear or mildly non-linear hyperspace partition functions. Hence, supervised le...

Full description

Bibliographic Details
Main Authors: David Medina-Ortiz, Sebastián Contreras, Cristofer Quiroz, Álvaro Olivera-Nappa
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-02-01
Series:Frontiers in Molecular Biosciences
Subjects:
highly non-linear datasets
supervised learning algorithms
clustering
statistical techniques
recursive binary methods
Online Access:https://www.frontiersin.org/article/10.3389/fmolb.2020.00013/full

Internet

https://www.frontiersin.org/article/10.3389/fmolb.2020.00013/full

Similar Items