View : 80 Download: 0

Averaged boosting: A noise-robust ensemble method

Title
Averaged boosting: A noise-robust ensemble method
Authors
Kim Y.
Ewha Authors
김용대
Issue Date
2003
Journal Title
Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
ISSN
0302-9743JCR Link
Citation
vol. 2637, pp. 388 - 393
Indexed
SCOPUS scopus
Abstract
A new noise robust ensemble method called "Averaged Boosting (A-Boosting)" is proposed. Using the hypothetical ensemble algorithm in Hilbert space, we explain that A-Boosting can be understood as a method of constructing a sequence of hypotheses and coefficients such that the average of the product of the base hypotheses and coefficients converges to the desirable function. Empirical studies showed that A-Boosting outperforms Bagging for low noise cases and is more robust than AdaBoost to label noise.
Appears in Collections:
자연과학대학 > 통계학전공 > Journal papers
Files in This Item:
There are no files associated with this item.
Export
RIS (EndNote)
XLS (Excel)
XML


qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE