Adjacent Inputs With Different Labels and Hardness in Supervised Learning

Adjacent Inputs With Different Labels and Hardness in Supervised Learning

An important aspect of the design of effective machine learning algorithms is the complexity analysis of classification problems. In this paper, we propose a study aimed at determining the relation between the number of adjacent inputs with different labels and the required number of examples for th...

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Bibliographic Details
Main Authors: Sebastian A. Grillo, Julio Cesar Mello Roman, Jorge Daniel Mello-Roman, Jose Luis Vazquez Noguera, Miguel Garcia-Torres, Federico Divina, Pedro Esteban Gardel Sotomayor
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Classification
data complexity
machine learning
overfitting
supervised learning
Online Access:https://ieeexplore.ieee.org/document/9627674/
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https://ieeexplore.ieee.org/document/9627674/

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