Tutorial series on brain-inspired computing: part 6: geometrical structure of boosting algorithm

Authors:
Takafumi Kanamori;Takashi Takenouchi;Noboru Murata
Affiliations:
Tokyo Institute of Technology, Tokyo, Japan;Nara Institute of Science and Technology, Nara, Japan;Waseda University, Tokyo, Japan
Venue:
New Generation Computing
Year:
2007

Citing 5
Cited 0

The Strength of Weak Learnability

Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting

Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Robust blind source separation by beta divergence

Neural Computation
Information geometry of U-Boost and Bregman divergence

Neural Computation
Robustifying AdaBoost by Adding the Naive Error Rate

Neural Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this article, several boosting methods are discussed, which are notable implementations of the ensemble learning. Starting from the firstly introduced "boosting by filter" which is an embodiment of the proverb "Two heads are better than one", more advanced versions of boosting methods "AdaBoost" and "U-Boost" are introduced. A geometrical structure and some statistical properties such as consistency and robustness of boosting algorithms are discussed, and then simulation studies are presented for confirming discussed behaviors of algorithms.