Yuan-chin Ivan Chang

Orcid: 0000-0002-4977-7721

Affiliations:
  • Academia Sinica, Taipei, Taiwan


According to our database1, Yuan-chin Ivan Chang authored at least 17 papers between 2002 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Active learning with logistic models featuring simultaneous variable and subject selection.
Commun. Stat. Simul. Comput., February, 2024

2023
Distributed sequential federated learning.
CoRR, 2023

2022
Determination of class-specific variables in nonparametric multiple-class classification.
CoRR, 2022

2021
A Model-Free Subject Selection Method for Active Learning Classification Procedures.
J. Classif., 2021

2020
Active learning in multiple-class classification problems via individualized binary models.
Comput. Stat. Data Anal., 2020

2019
Active learning with simultaneous subject and variable selections.
Neurocomputing, 2019

Greedy active learning algorithm for logistic regression models.
Comput. Stat. Data Anal., 2019

Active learning for binary classification with variable selection.
CoRR, 2019

Fast Multi-Class Probabilistic Classifier by Sparse Non-parametric Density Estimation.
CoRR, 2019

2017
Multiple-class classification: Ordinal and categorical labels.
Commun. Stat. Simul. Comput., 2017

2016
Assessing the diagnostic power of variables measured with a detection limit.
Comput. Stat., 2016

2010
Early stopping in L<sub>2</sub>Boosting.
Comput. Stat. Data Anal., 2010

2009
Meta-learning for imbalanced data and classification ensemble in binary classification.
Neurocomputing, 2009

2007
A stochastic approximation view of boosting.
Comput. Stat. Data Anal., 2007

A parsimonious threshold-independent protein feature selection method through the area under receiver operating characteristic curve.
Bioinform., 2007

2004
Synergy of Logistic Regression and Support Vector Machine in Multiple-Class Classification.
Proceedings of the Intelligent Data Engineering and Automated Learning, 2004

2002
Graphical Features Selection Method.
Proceedings of the Intelligent Data Engineering and Automated Learning, 2002


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