Skip to content
Descubridor Institucional UMAR
Inicio
Búsqueda avanzada
Explorar
Inicio
Búsqueda avanzada
Explorar
Login
Language
English
Deutsch
Español
Français
Italiano
All Fields
Title
Author
Subject
Call Number
ISBN/ISSN
Tag
Find
Advanced
Quantifying Epistemic Uncertainty in Binary Classification via Accuracy Gain
Quantifying Epistemic Uncertainty in Binary Classification via Accuracy Gain
Fuente:
Wiley Open Access
Saved in:
Bibliographic Details
Main Authors:
Christopher Qian
,
Tyler Ganter
,
Joshua Michalenko
,
Feng Liang
,
Jason Adams
Format:
Artículo Open Access
Published:
Wiley
2024
Subjects:
Statistical Analysis and Data Mining: The ASA Data Science Journal
Online Access:
Acceder al recurso
Tags:
Add Tag
No Tags, Be the first to tag this record!
Cite this
Text this
Email this
Print
Export Record
Export to RefWorks
Export to EndNoteWeb
Export to EndNote
Save to List
Permanent link
Holdings
Description
Comments
Similar Items
Staff View
Be the first to leave a comment!
Your Comment
You must be logged in first
Similar Items
Wasserstein Centroid‐Based Binary Classification for Distributional Data
by: Seokgeon Jang, et al.
Published: (2025)
A Homogeneity Test for Ordinal Receiver Operating Characteristic Regression With Application to Facial Recognition Accuracy Assessment
by: Ngoc‐Ty Nguyen, et al.
Published: (2025)
Robust Model‐Based Semi‐Supervised Clustering of Incomplete Records
by: Joshua D. Berlinski, et al.
Published: (2025)
Computational Improvements to the Kernel k$$ k $$‐Means Clustering Algorithm
by: Joshua D. Berlinski, et al.
Published: (2025)
Persistent Classification: Understanding Adversarial Attacks by Studying Decision Boundary Dynamics
by: Brian Bell, et al.
Published: (2025)