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Description: adaboost分类器源代码，由若干弱分类器进行组合-adaboost classifier source code, a number of weak classifier combination
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Author: 赵培 |
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Description: Boosting is a meta-learning approach that aims at combining an ensemble of weak classifiers to form a strong classifier. Adaptive Boosting (Adaboost) implements this idea as a greedy search for a linear combination of classifiers by overweighting the examples that are misclassified by each classifier. icsiboost implements Adaboost over stumps (one-level decision trees) on discrete and continuous attributes (words and real values). See http://en.wikipedia.org/wiki/AdaBoost and the papers by Y. Freund and R. Schapire for more details [1]. This approach is one of most efficient and simple to combine continuous and nominal values. Our implementation is aimed at allowing training from millions of examples by hundreds of features in a reasonable time/memory.
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Author: njustyw |
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Description: AdaBoost is an efficient tool in machine learning. It can combine a series of weak learners into a strong learner. Besides pattern classification, it also can be applied into feature selection. This document explains the use of AdaBoost.
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Author: njustyw |
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Description: adaboost 弱分类器构成强分类器算法，并作图，分析样本数对性能的影响-constitute a weak classifier AdaBoost strong classifier algorithm, and mapping, analysis of sample size on the performance of
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Author: 毛楠 |
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Description: 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.
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Author: wangxuyang |
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Description: it is an adaboost weak learner
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Author: steve |
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Description: adaboost training for select weak classifier.
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Author: tuzhiguo |
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Description: 人脸检测。用c++编写。人脸检测是人脸分析的首要环节，其处理的问题是确认图像（或影像）中是
否存在人脸，如果存在则对人脸进行定位。人脸检测的应用领域相当广泛，是实
现机器智能化的重要步骤之一。
AdaBoost 算法是1995 年提出的一种快速人脸检测算法，是人脸检测领域里
程碑式的进步，这种算法根据弱学习的反馈，适应性地调整假设的错误率，使在
效率不降低的情况下，检测正确率得到了很大的提高。-Face Detection. Using c++ to prepare. Face detection is the most important aspect of facial analysis, which dealt with the problem is to identify the image (or images) in the presence of human faces, if there is to locate human faces. Face detection applications of a wide range of intelligent machines to achieve one of the important steps. AdaBoost algorithm is proposed in 1995 a fast face detection algorithms, Face Detection is a landmark of progress in the field, the weak learning algorithm based on feedback, adaptive adjustment of the error rate assumptions, so that efficiency is not reduced in the case of , the detection accuracy has been greatly improved.
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Author: ashura |
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Description: Video tracking using Adaboost algorithm for auto-updating of the weak classifiers.
The object has to be defined by the user (no auto-recogntion). The tracker works for every type of object (no prior assumptions are taken to object size or form).
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Author: yosko |
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Description: adaboost源码Adaboost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 source is an iterative algorithm Adaboost, the core idea is that a training set for the same training the different classifiers (weak classifier), then these weak classifiers together to form a stronger final classifier (strong classification device).
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Author: ado |
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Description: 此资料实现了ADABOOST和HAAR的特征提取和从弱分类到强分类的具体步骤-The data achieved ADABOOST and HAAR of feature extraction and classification from weak to strong category specific steps
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Author: shuyachen |
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Description: 最经典AdaBoost实现，适合初学，有大量详细的注释，容易理解-This a classic AdaBoost implementation, in one single file with easy understandable code.
The function consist of two parts a simple weak classifier and a boosting part:
The weak classifier tries to find the best threshold in one of the data dimensions to separate the data into two classes-1 and 1
The boosting part calls the classifier iteratively, after every classification step it changes the weights of miss-classified examples. This creates a cascade of "weak classifiers" which behaves like a "strong classifier" 。
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Author: wj |
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Description: adaboost算法训练,tr_func_handle and te_func_handle are function handles for
training and testing of a weak learner, respectively. The weak learner
has to support the learning in weighted datasets. The prototypes
of these functions has to be as follows.-adaboost train
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Author: raymond |
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Description: adaboost 测试，te_func_handle is a handle to the testing function of a
learning (weak) algorithm whose prototype is shown below.-te_func_handle is a handle to the testing function of a
learning (weak) algorithm whose prototype is shown below.
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Author: raymond |
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Description: AdaBoost 是一种将弱分类器线性结合成强分类器的方法，由Jiri Matas 和 Jan Sˇ ochman提出-AdaBoost is a linear combination of the weak classifiers into a strong classifier method, by Jiri Matas and Jan S ochman proposed
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Author: 董晶晶 |
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Description: 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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Author: 丁丁 |
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Description: adaboost 源码，综合多个弱分类器，通过动态修改元组权值以及得到的分类器权值，提升为复合强分类器。-AdaBoost source code, several weak classifiers, by dynamically modifying the tuple classifier obtained by weight and weight, enhance the strong classifier for composite.
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Author: ss |
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Description: 这是一本介绍AdaBoost算法的资料。Adaboost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-This is an introduction AdaBoost algorithm information. Adaboost is an iterative algorithm, the core idea for a training set of different classifiers (weak classifier), then these weak classifiers together to form a stronger final classifier
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Description: 以二叉树作为弱分类器的adaboost的matlab算法代码-Binary tree as weak classifier adaboost matlab algorithm code
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Author: zhanglong |
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Description: 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 final classifier. The algorithm is achieved by changing the data distribution.)
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Author: 廿四包
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