ϵ-Confidence Approximately Correct (ϵ-CoAC) Learnability and Hyperparameter Selection in Linear Regression Modeling
In a data based learning process, training data set is utilized to provide a hypothesis that can be generalized to explain all data points from a domain set. The hypothesis is chosen from classes with potentially different complexities. Linear regression modeling is an important category of learning...
Saved in:
Main Authors: | Soosan Beheshti, Mahdi Shamsi |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2025-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10840229/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Assessing the Impact of Physical Activity on Dementia Progression Using Clustering and the MRI-Based Kullback–Leibler Divergence
by: Agnieszka Wosiak, et al.
Published: (2025-01-01) -
The effect of interspike interval statistics on the information gainunder the rate coding hypothesis
by: Shinsuke Koyama, et al.
Published: (2013-08-01) -
Relative information spectra with applications to statistical inference
by: Sergio Verdú
Published: (2024-12-01) -
Continuity of the maps f↦∪x∈Iω(x,f) and f↦{ω(x,f):x∈I}
by: T. H. Steele
Published: (2006-01-01) -
Expression of T-Cell Receptor β-Chain mRNA and Proteinin γ/δ T-Cells from Euthymic and Athymic Rats:Implications for T-Cell Lineage Divergence
by: Astrid Bischof, et al.
Published: (2000-01-01)