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AdaBoost, Adaptive Boosting, is a well-known meta machine learning algorithm that was proposed by Yoav Freund and Robert Schapire. In this project there two main files
1. ADABOOST_tr.m
2. ADABOOST_te.m
to traing and test a user-coded learnin
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用adaboost算法生成基支持向量机分类器，并对识别结果进行简单投票法集成。附有支持向量机工具箱和adaboost算法流程说明。-Adaboost algorithm to generate the base with a support vector machine classifier, and the recognition result is a simple voting method integration. With support vector machine algorith
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matable下编的一个算法！线性阈值分类器运用AdaBoost算法！-An Algorithm for under matable! The use of linear threshold classifier AdaBoost algorithm!
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采用matlab实现了Adaboost分类器算法，有利于学生对分类器的学习！-The function of this code is to implement the Adaboost algorithm using matlab programming language,which is very useful to learn of all kinds of classifier for students, especially for beginners.
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Adaboost算法是一类弱分类器，本算法使用adaboost算法实现对于训练数据划分成两个组，然后可以用来进行数据的分类。-Adaboost algorithm is a class of weak classifier, the algorithm uses adaboost algorithm for the training data into two groups, and then can be used for data classification.
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提供了adaboost Algoritm的工具箱，该算法是一种迭代算法，其核心思想是针对同一个训练集训练不同的分类器(弱分类器)，然后把这些弱分类器集合起来，构成一个更强的最终分类器(强分类器)。
-Adaboost is an iterative algorithm, the core idea is the same training set for training the different classifiers (weak classifier), then these weak
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will apply adaboost algorithm for making a strong classifier.
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adaboost智能算法源程序，将若分类器反复训练得到强分类器，用于二分类问题-adaboost intelligent algorithm source code, will be repeated if the classifier has been trained strong classifier for the two classification
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实现简单的adaboost算法来产生强分类器-The simple adaboost algorithm to produce a strong classifier
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daboost是一种迭代算法，其核心思想是针对同一个训练集训练不同的分类器(弱分类器)，然后把这些弱分类器集合起来，构成一个更强的最终分类器 (强分类器)。-daboost is an iterative algorithm, the core idea is the same training set different classifiers (weak classifiers), and then these weak classifiers together to form a stro
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Adaboost算法，广泛使用与人脸识别功能。通过若干弱分类器合成强分类器。-Adaboost algorithm, widely used and face recognition feature. Synthesized by a number of weak classifiers strong classifier.
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其核心思想是针对同一个训练集训练不同的分类器(弱分类器)，然后把这些弱分类器集合起来，构成一个更强的最终分类器(强分类器)。其算法本身是通过改变数据分布来实现的(Adaboost as an iterative algorithm which core idea is to train several different weak classifiers for the same dataset and then collect them together to form a stronger
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基于码本（codebook）的背景建模的背景差分法+级联基于LBK或haar的adaboost和基于hog的svm分类器+快速hough圆变换进行人头识别+基于区域特征的目标跟踪算法。(编程)
AdaBoost是一种增强性机器学习算法，它用于把弱分类器联合成强分类器；SVM本身就是(Background modeling based on codebook (codebook) background difference method + cascade based on LBK or Haa
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基于码本（codebook）的背景建模的背景差分法+级联基于LBK或haar的adaboost和基于hog的svm分类器+快速hough圆变换进行人头识别+基于区域特征的目标跟踪算法。(编程)(Background modeling based on codebook (codebook) background difference method + cascade based on LBK or Haar AdaBoost and hog based SVM Classifier + fast
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基于码本（codebook）的背景建模的背景差分法+级联基于LBK或haar的adaboost和基于hog的svm分类器+快速hough圆变换进行人头识别(Background modeling based on codebook (codebook) background difference method + cascade based on LBK or Haar AdaBoost and hog based SVM Classifier + fast Hough circle trans
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基于码本（codebook）的背景建模的背景差分法+级联基于LBK或haar的adaboost和基于hog的svm分类器+快速hough圆变换进行人头识别+基于区域特征的目标跟踪算法。(编程)
AdaBoost是一种增强性机器学习算法，它用于把弱分类器联合成强分类分类器(Background modeling based on codebook (codebook) background difference method + cascade based on LBK or Haar AdaB
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基于matlab平台的集成学习算法，基分类器为决策树的adaboost(An integrated learning algorithm based on MATLAB platform, the base classifier is AdaBoost of decision tre)
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