Novel Considerations in the ML/AI Modeling of Large-Scale Learning Loss

Novel Considerations in the ML/AI Modeling of Large-Scale Learning Loss

This study is a path forward for the large-scale, data-driven quantitative analysis of noisy open-source data resources. The goal is to support qualitative findings of smaller studies with extensive open-source data-driven analytics in a new way. The study presented in this research focuses on learn...

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Bibliographic Details
Main Authors: Mirna Elizondo, June Yu, Daniel Payan, LI Feng, Jelena Tesic
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Noisy tabular data
data in the wild
gradient boosting
feature selection
dimensionality reduction
Online Access:https://ieeexplore.ieee.org/document/10829573/
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