.png){kind=icon}
返回
Quadratic boosting
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2007.05.008.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper presents a strategy to improve the AdaBoost algorithm with a quadratic combination of base classifiers. We observe that learning this combination is necessary to get better performance and is possible by constructing an intermediate learner operating on the combined linear and quadratic terms. This is not trivial, as the parameters of the base classifiers are not under direct control, obstructing the application of direct optimization. We propose a new method realizing iterative optimization indirectly. First we train a classifier by randomizing the labels of training examples. Subsequently, the input learner is called repeatedly with a systematic update of the labels of the training examples in each round. We show that the quadratic boosting algorithm converges under the condition that the given base learner minimizes the empirical error. We also give an upper bound on the VC-dimension of the new classifier. Our experimental results on 23 standard problems show that quadratic boosting compares favorably with AdaBoost on large data sets at the cost of training speed. The classification time of the two algorithms, however, is equivalent. (C) 2007 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
Keyword:
AdaBoost
boosting algorithm
coordinate descent
generalization error
object detection
quadratic boosting
randomized relabeling
VC-dimension
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
暂无机构信息
引用论文
.png){kind=icon}
Thiol-functionalized silica colloids, grains, and membranes for irreversible adsorption of metal(oxide) nanoparticles用于金属 (氧化物) 纳米颗粒不可逆吸附的硫醇官能化二氧化硅胶体，颗粒和膜
OAAI
OAAI分享收藏
A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting在线学习的决策理论概括及其在Boosting中的应用OAAI
OAAI分享收藏
查看更多内容