文件名称:simpleABdemo
- 所属分类:
- 图形/文字识别
- 资源属性:
- [Matlab] [源码]
- 上传时间:
- 2012-11-26
- 文件大小:
- 8kb
- 下载次数:
- 0次
- 提 供 者:
- wangx*****
- 相关连接:
- 无
- 下载说明:
- 别用迅雷下载,失败请重下,重下不扣分!
介绍说明--下载内容均来自于网络,请自行研究使用
Adaboost算法的基本思想是:利用大量的分类能力一般的弱分类器(weaker
ifier)通过一定的方法叠加(boost)起来,构成一个分类能力很强的强分类器
眼eClassifier),再将若干个强分类器串联成为分级分类器(ClassifierCaseade)
图像搜索检测。本文就是利用Adaboost算法将由类haar特征生成的弱分类器
成为强分类器,再将强分类器串联成为分级分类器。
-Adaboost algorithm basic idea is: the ability to use the general classification of a large number of weak classifier (weakerifier) through a certain method of superposition (boost), and constitute a strong Category strong classifier eyes eClassifier), then a number of strong Category connected in series to become hierarchical classifier (ClassifierCaseade) image search detection. Adaboost algorithm is to use this article will be generated characteristics category haar weak classifier become strong classifier, and then become a strong tandem Classifier Classifier classification.
ifier)通过一定的方法叠加(boost)起来,构成一个分类能力很强的强分类器
眼eClassifier),再将若干个强分类器串联成为分级分类器(ClassifierCaseade)
图像搜索检测。本文就是利用Adaboost算法将由类haar特征生成的弱分类器
成为强分类器,再将强分类器串联成为分级分类器。
-Adaboost algorithm basic idea is: the ability to use the general classification of a large number of weak classifier (weakerifier) through a certain method of superposition (boost), and constitute a strong Category strong classifier eyes eClassifier), then a number of strong Category connected in series to become hierarchical classifier (ClassifierCaseade) image search detection. Adaboost algorithm is to use this article will be generated characteristics category haar weak classifier become strong classifier, and then become a strong tandem Classifier Classifier classification.
(系统自动生成,下载前可以参看下载内容)
下载文件列表
ABdemo
......\AdaBoost.m
......\AdaBoostClassify.m
......\SingleWeakLearner.m
......\SingleWeakLearnerROC.m
......\StrongClassify.m
......\TestAdaBoost.m
......\Toy.m
......\WeakClassify.m
......\WeakClassifyBatch.m
......\WeakClassifyROC.m
......\WeakLearner.m
......\AdaBoost.m
......\AdaBoostClassify.m
......\SingleWeakLearner.m
......\SingleWeakLearnerROC.m
......\StrongClassify.m
......\TestAdaBoost.m
......\Toy.m
......\WeakClassify.m
......\WeakClassifyBatch.m
......\WeakClassifyROC.m
......\WeakLearner.m