February 15, 2012Open Access

Predicting sample size required for classification performance

Key Points

Key points are not available for this paper at this time.

Abstract

This paper describes a simple and effective sample size prediction algorithm that conducts weighted fitting of learning curves. The algorithm outperformed an un-weighted algorithm described in previous literature. It can help researchers determine annotation sample size for supervised machine learning.

Connected Papers

Building similarity graph...

Analyzing shared references across papers

Discussion

Authors

Rosa L. Figueroa

University of California, Davis

Qing Zeng‐Treitler

United States Department of Veterans Affairs

Sasikiran Kandula

Norwegian Institute of Public Health

Journals

BMC Medical Informatics and Decision Making

SHILAP Revista de lepidopterología

Actions

Institutions

Harvard University

University of Utah

Beth Israel Deaconess Medical Center

References and Citations

Connected Papers

Building similarity graph...

Analyzing shared references across papers

Predicting sample size required for classification performance

Key Points

Abstract

Citation Network

Connected Papers

Discussion

Authors

Journals

Actions

Institutions

References and Citations

Citation Network

Connected Papers

Discussion

Cite this study

Also consider

Also consider