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...
Saved in:
| 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: | |
| Online Access: | https://ieeexplore.ieee.org/document/9627674/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
COVID-19 Data Analysis: The Impact of Missing Data Imputation on Supervised Learning Model Performance
by: Jorge Daniel Mello-Román, et al.
Published: (2025-03-01) -
Structured insight: an innovative disambiguation paradigm for semi-supervised partial label learning
by: Xin Niu, et al.
Published: (2025-07-01) -
Unsupervised selective labeling for semi-supervised industrial defect detection
by: Jian Ge, et al.
Published: (2024-10-01) -
Weak Labeling-specific Emitter Identification Algorithm Based on the Weakly Supervised Wav-KAN Network
by: Kangsheng LIU, et al.
Published: (2025-04-01) -
Learning Self-Supervised Representations of Powder-Diffraction Patterns
by: Shubhayu Das, et al.
Published: (2025-04-01)