View : 753 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
- 0302-9743
- Citation
- Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) vol. 2637, pp. 388 - 393
- Indexed
- SCOPUS
- Document Type
- Conference Paper
- 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