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

Takafumi Kanamori; Takashi Takenouchi; Noboru Murata

doi:10.1007/s00354-006-0006-0

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

Takafumi Kanamori^*, Takashi Takenouchi, Noboru Murata

^*Corresponding author for this work

School of Advanced Science and Engineering

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

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.

Original language	English
Pages (from-to)	117-141
Number of pages	25
Journal	New Generation Computing
Volume	25
Issue number	1
DOIs	https://doi.org/10.1007/s00354-006-0006-0
Publication status	Published - 2007 Jan 24

Keywords

Boosting
Classification problem
Large-scale learning machine
Statistical learning theory

ASJC Scopus subject areas

Software
Theoretical Computer Science
Hardware and Architecture
Computer Networks and Communications

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10.1007/s00354-006-0006-0

Cite this

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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.",

keywords = "Boosting, Classification problem, Large-scale learning machine, Statistical learning theory",

author = "Takafumi Kanamori and Takashi Takenouchi and Noboru Murata",

year = "2007",

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AU - Takenouchi, Takashi

AU - Murata, Noboru

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Y1 - 2007/1/24

N2 - 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.

AB - 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.

KW - Boosting

KW - Classification problem

KW - Large-scale learning machine

KW - Statistical learning theory

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

Abstract

Keywords

ASJC Scopus subject areas

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