多阈值划分的连续AdaBoost人脸检测

连续AdaBoost算法要求对样本空间进行划分,传统的等距划分无法体现正负样本各自的分布规律.对基于连续AdaBoost算法的人脸检测方法进行了改进,结合离散AdaBoost中弱分类器的阈值选取策略,通过多重最优阈值选择方法实现了样本空间的合理划分.在MIT-CBCL数据库上的实验结果表明,改进后的方法比等距划分和连续AdaBoost算法检测率提高0.5%和2%,错误率降低0.15%和0.27%,算法收敛速度更快.     

基于样本噪声检测的AdaBoost算法改进

针对传统的AdaBoost算法中,存在的噪声样本造成的过拟合问题,提出了一种基于噪声检测的AdaBoost改进算法,本文称为NAdaBoost（nois-detection AdaBoost）. NAdaBoost算法创新点在于针对传统的AdaBoost算法在错误分类的样本中,噪声样本在某些属性上存在很大差异,根据这一特性来确定噪声样本,再重新使用算法对两类样本进行分类,最终达到提高分类准确率的目的. 本文对二分类问题进行实验结果表明,本文提出的算法和传统的AdaBoost算法,以及相关改进的算法相比,有较高的分类准确率.     

基于代价敏感的AdaBoost算法改进

针对传统的AdaBoost算法只关注分类错误率最小的问题,在分析传统的AdaBoost算法实质基础上,提出一种基于代价敏感的改进AdaBoost算法。首先在训练基分类器阶段,对于数据集上的不同类别样本根据其错分后造成的损失大小不同来更新样本权值,使算法由关注分类错误率最小转而关注分类代价最小。然后,在组合分类器输出时采用预测概率加权方法来取代传统AdaBoost算法采用的预测类别加权的方法。最后通过实验验证了改进算法的有效性。     

视频序列图像中的人脸检测与跟踪

目标检测与跟踪是计算机视觉领域最普遍和关键的问题之一。基于级联结构的AdaBoost算法和基于色彩的Camshift算法,目前被认为是较有效的检测和跟踪算法,结合AdaBoost、Camshift算法和Kalman滤波器实现,对视频序列图像中多角度人脸的实时检测与跟踪,并针对AdaBoost训练耗时问题提出了改进。     

基于AdaBoost分类器的车牌字符识别算法研究*

提出了一种基于模板匹配的改进AdaBoost算法的字符识别方法。针对AdaBoost算法的退化现象,根据样本在权重上的分布情况,对权重进行适当的调整,较好地解决了经典 AdaBoost算法中存在的退化现象。最后给出了实验结果及实现方案。     

人脸检测级联分类器快速训练算法

目前AdaBoost训练算法已被广泛地应用于人脸检测中级联分类器的构建,而AdaBoost算法训练级联分类器的周期却十分漫长.为了减少训练时间,文中提出了一种基于AdaBoost的改进训练算法.该算法通过对弱分类器的阈值选择进行一趟处理来降低运算时间复杂度,并根据AdaBoost训练迭代中只改变样本权值而不更新样本的特点对特征值和排序结果进行缓存来提高训练算法的性能.实验结果表明,该算法大幅提高了人脸检测分类器训练系统的性能,使得分类器的训练时间缩短了60多倍.由于AdaBoost算法的通用性,该改进算法不仅适用于人脸检测,也适合所有进行权值更新迭代训练的Boosting算法.     

一种改进的AdaBoost算法——AD AdaBoost

目标检测问题是计算机视觉领域最普遍和关键的问题之一.基于级联结构的AdaBoost算法目前被认为是较有效的检测算法,但是其在低FRR端的性能仍需改进.文章提出了一种针对目标检测问题的改进AdaBoost算法--AD AdaBoost.AD AdaBoost采用了新的参数求解方法,弱分类器的加权参数不但与错误率有关,还与其对正样本的识别能力有关.该算法能够有效地降低分类器在低FRR端的FAR,使其更适用于目标检测问题.新旧算法在复杂背景中文字检测的实验结果对比证实了新算法在性能上的改进.     

基于PCA+AdaBoost算法的人脸识别技术

人脸识别技术是图像处理方面的重要技术,然而识别率不高却一直妨碍人脸识别技术的广泛应用。主成分分析（PCA）是人脸识别技术的一个重要算法,将PCA与AdaBoost算法相结合改进了原来的算法,并称新算法为PCA+AdaBoost算法。实验证明PCA+AdaBoost算法的识别率明显高于PCA算法,相对于Fisherface算法的识别率也有明显的提高。     

基于BP神经网络的人脸检测AbaBoost算法

人脸检测在日常生产和应用非常重要。本文提出了一种基于BP神经网络的AdaBoost人脸检测算法。首先,使用BP神经网络代替YCbCr高斯模型建立肤色模型。同时,针对AdaBoost算法提出了一种新的权值更新方法。在权值更新中引入阈值与样本之间的距离。另外权重有一个边界值。最后,利用BP神经网络提取图像中的肤色候选区域,并采用改进的AdaBoost算法对图像中的人脸进行精确检测。实验结果表明,利用BP神经网络和改进的AdaBoost算法的新的解决方案比现有的方法具有更高的精度。     

基于AdaBoost分类器的车牌字符识别算法研究

提出了一种基于模板匹配的改进AdaBoost算法的字符识别方法。针对AdaBoost算法的退化现象，根据样本在权重上的分布情况，对权重进行适当的调整，较好地解决了经典AdaBoost算法中存在的退化现象：最后给出了实验结果及实现方案。     

Remote-sensing imagery classification using multiple classification algorithm-based AdaBoost

AdaBoost demonstrates excellent performance in remote sensing (RS) image classification, but as it works on only one classification algorithm, the disadvantage of the classification algorithm itself is difficult to overcome, resulting in limitations in the improvement of classification accuracy. In this article, a modified AdaBoost, a multiple classification algorithm-based AdaBoost (MCA AdaBoost), is proposed to improve remote sensing image classification. The new method works on more than one classification algorithm and can make full use of the advantages of different learning algorithms. Based on a Landsat 8 Operational Land Imager (OLI) image whose spatial resolution was enhanced to 15 m with a panchromatic band, a C4.5 decision tree, Naïve Bayes, and artificial neural network were used as objects to verify and compare the performance of both AdaBoost and MCA AdaBoost. The experimental results show that MCA AdaBoost successfully inherits the benefits of the original AdaBoost, combines the advantages of different classification algorithms and lowers overfitting. By increasing diversity and complementarity among base classifiers, MCA AdaBoost outperforms AdaBoost in terms of RS classification accuracy improvement.     

AdaBoost算法的一种改进方法

Boosting是一种改善任意给定的机器学习算法准确性的通用方法．主要针对AdaBoost算法，介绍了AdaBoost算法的研究背景．分析了实验过程中出现的退化问题以及目标类权重分布扭曲的现象，提出了一种基于调整权重分布，限制权重扩张的改进方法，最后给出了实验结果和分析．     

Improvement of Boosting Algorithm by Modifying the Weighting Rule

AdaBoost is a method for improving the classification accuracy of a given learning algorithm by combining hypotheses created by the learning alogorithms. One of the drawbacks of AdaBoost is that it worsens its performance when training examples include noisy examples or exceptional examples, which are called hard examples. The phenomenon causes that AdaBoost assigns too high weights to hard examples. In this research, we introduce the thresholds into the weighting rule of AdaBoost in order to prevent weights from being assigned too high value. During learning process, we compare the upper bound of the classification error of our method with that of AdaBoost, and we set the thresholds such that the upper bound of our method can be superior to that of AdaBoost. Our method shows better performance than AdaBoost.     

A geometric approach to leveraging weak learners

AdaBoost is a popular and effective leveraging procedure for improving the hypotheses generated by weak learning algorithms. AdaBoost and many other leveraging algorithms can be viewed as performing a constrained gradient descent over a potential function. At each iteration the distribution over the sample given to the weak learner is proportional to the direction of steepest descent. We introduce a new leveraging algorithm based on a natural potential function. For this potential function, the direction of steepest descent can have negative components. Therefore, we provide two techniques for obtaining suitable distributions from these directions of steepest descent. The resulting algorithms have bounds that are incomparable to AdaBoost's. The analysis suggests that our algorithm is likely to perform better than AdaBoost on noisy data and with weak learners returning low confidence hypotheses. Modest experiments confirm that our algorithm can perform better than AdaBoost in these situations.     

基于AdaBoost.M2和神经模糊系统的植物识别算法

为提高传统神经模糊系统（NFS）在植物识别领域对于相似植物样本的识别能力,提出了AdaBoost.M2-NFS算法。该算法首先对传统NFS进行改进以便融合,然后将新NFS与AdaBoost.M2结合得到AdaBoost.M2-NFS新模型。在Iris数据集上实验结果表明：新模型与单个NFS相比,识别率增加了3.33个百分点;与线性支持向量机（SVM）相比,识别率增加了1.11个百分点;与Softmax相比,识别率增加了3.33个百分点。根据敏感性和特异性分析可知,所提模型对于线性不可分数据分类效果比对线性可分数据分类效果好;同时,由于AdaBoost.M2的改进,使得所提算法在植物识别领域具备快速成型和高泛化能力。     

Boosting an associative classifier

Associative classification is a new classification approach integrating association mining and classification. It becomes a significant tool for knowledge discovery and data mining. However, high-order association mining is time consuming when the number of attributes becomes large. The recent development of the AdaBoost algorithm indicates that boosting simple rules could often achieve better classification results than the use of complex rules. In view of this, we apply the AdaBoost algorithm to an associative classification system for both learning time reduction and accuracy improvement. In addition to exploring many advantages of the boosted associative classification system, this paper also proposes a new weighting strategy for voting multiple classifiers.