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

被引：0
|
作者：
Kanamori, Takafumi
Takenouchi, Takashi
Murata, Noboru
机构：
[1] Tokyo Inst Technol, Meguro Ku, Tokyo 1528550, Japan
[2] Nara Inst Sci & Technol, Nara 6300192, Japan
[3] Waseda Univ, Shinjuku Ku, Tokyo 1698555, Japan
来源：
NEW GENERATION COMPUTING | 2007年 / 25卷 / 01期
关键词：
boosting; classification problem; large-scale learning machine; statistical learning theory;
D O I：
暂无
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：117 / 141
页数：25
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