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Search Results: 1 - 10 of 5360 matches for " 连续AdaBoost "
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基于ds-adaboost算法的人脸检测
叶俊?,张正军?
计算机科学 , 2013,
Abstract: 针对连续adaboost算法中平滑因子选取的不足,提出了一种动态选取平滑因子的ds-adaboost算法,该算法对弱分类器输出中的平滑因子ε进行了动态选取,根据wj+1wj-1比值的大小动态地选择平滑因子,当wj+1wj-1>1时,εj=wj+1,当0j+1wj-1<1时,εj=wj-1。实验表明,ds-adaboost算法能较好地起到平滑的作用,使得落在同一个区间里面的正样本和负样本的比例都在可以比拟的范围内。
一种融合LBP纹理特征的多姿态人脸跟踪方法
陈远,陈锻生
华侨大学学报(自然科学版) , 2010, DOI: 10.11830/ISSN.1000-5013.2010.03.0282
Abstract: 提出一种改进的Camshift算法,它融合目标人脸的局部二值模式(LBP)纹理特征的T分量,以及肤色的HSV色彩空间的H分量的统计直方图来生成概率分布图像,实现纹理与肤色特征的有效融合;然后,利用Kalman滤波器来预测目标人脸的运动信息,快速地跟踪到目标人脸.实验表明,在复杂的跟踪条件下,这种算法比原始的仅采用颜色直方图信息的Meanshift和Camshift算法,在跟踪速度和精度上有显著的提高.
多阈值划分的连续adaboost人脸检测
孙士明,潘青,纪友芳
计算机应用 , 2009,
Abstract: ?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。
连续型adaboost算法研究
严超,王元庆?
计算机科学 , 2010,
Abstract: 现阶段的人工智能与模式识别工作中,连续型adaboost算法以其良好的识别率和极快的识别速度得到了越来越多的应用。鉴于此,认真研究了连续型adaboost算法的理论基础,细致分析了基于连续型adaboost算法的分类器的训练流程,对算法中涉及到的数学量之间的关系进行了探讨,对算法中涉及到的数学过程进行了定量推导,对训练过程中出现的问题的成因进行了定性分析,最后对如何提高连续型adaboost算法的性能提出了若干建议。
Research of the Real Adaboost Algorithm
连续型Adaboost算法研究

YAN Chao,WANG Yuan-qing,
严超
,王元庆

计算机科学 , 2010,
Abstract: In the current artificial intelligence and pattern recognition, Real Adaboost Algorithm, as for high accuracy rate and very fast specd,has been used more widely. As a result, we researched the theoretical basis of the Real Adaboost Algorithm conscientiously and analyzed the training procedures of classifiers based on the Real Adaboost Algorithm meticulously. In this course, we probed into the relationship between the mathematical variables involved in the algorithm; deduced the mathematical process involved in the algorithm quantitatively, and analyzed the reasons of problems appearing in training procedures qualitatively. At last, in order to improve the Real Adaboost Algorithm, we brought up several suggestions.
多分类问题代价敏感AdaBoost算法
付忠良
自动化学报 , 2011, DOI: 10.3724/SP.J.1004.2011.00973
Abstract: ?针对目前多分类代价敏感分类问题在转换成二分类代价敏感分类问题存在的代价合并问题,研究并构造出了可直接应用于多分类问题的代价敏感AdaBoost算法.算法具有与连续AdaBoost算法类似的流程和误差估计.当代价完全相等时,该算法就变成了一种新的多分类的连续AdaBoost算法,算法能够确保训练错误率随着训练的分类器的个数增加而降低,但不直接要求各个分类器相互独立条件,或者说独立性条件可以通过算法规则来保证,但现有多分类连续AdaBoost算法的推导必须要求各个分类器相互独立.实验数据表明,算法可以真正实现分类结果偏向错分代价较小的类,特别当每一类被错分成其他类的代价不平衡但平均代价相等时,目前已有的多分类代价敏感学习算法会失效,但新方法仍然能实现最小的错分代价.研究方法为进一步研究集成学习算法提供了一种新的思路,得到了一种易操作并近似满足分类错误率最小的多标签分类问题的AdaBoost算法.
基于浮动阈值分类器组合的多标签分类算法
张丹普,付忠良,王莉莉,李昕
计算机应用 , 2015,
Abstract: ?针对目标可以同时属于多个类别的多标签分类问题,提出了一种基于浮动阈值分类器组合的多标签分类算法.首先,分析探讨了基于浮动阈值分类器的adaboost算法(adaboost.ft)的原理及错误率估计,证明了该算法能克服固定分段阈值分类器对分类边界附近点分类不稳定的缺点从而提高分类准确率;然后,采用二分类(br)方法将该单标签学习算法应用于多标签分类问题,得到基于浮动阈值分类器组合的多标签分类方法,即多标签adaboost.ft.实验结果表明,所提算法的平均分类精度在emotions数据集上比adaboost.mh、ml-knn、ranksvm这3种算法分别提高约4%、8%、11%;在scene、yeast数据集上仅比ranksvm低约3%、1%.由实验分析可知,在不同类别标记之间基本没有关联关系或标签数目较少的数据集上,该算法均能得到较好的分类效果.
HumanBoost: Utilization of Users’ Past Trust Decision for Identifying Fraudulent Websites  [PDF]
Daisuke Miyamoto, Hiroaki Hazeyama, Youki Kadobayashi
Journal of Intelligent Learning Systems and Applications (JILSA) , 2010, DOI: 10.4236/jilsa.2010.24022
Abstract: This paper presents HumanBoost, an approach that aims at improving the accuracy of detecting so-called phishing sites by utilizing users’ past trust decisions (PTDs). Web users are generally required to make trust decisions whenever their personal information is requested by a website. We assume that a database of user PTDs would be transformed into a binary vector, representing phishing or not-phishing, and the binary vector can be used for detecting phishing sites, similar to the existing heuristics. For our pilot study, in November 2007, we invited 10 participants and performed a subject experiment. The participants browsed 14 simulated phishing sites and six legitimate sites, and judged whether or not the site appeared to be a phishing site. We utilize participants’ trust decisions as a new heuristic and we let AdaBoost incorporate it into eight existing heuristics. The results show that the average error rate for HumanBoost was 13.4%, whereas for participants it was 19.0% and for AdaBoost 20.0%. We also conducted two follow-up studies in March 2010 and July 2010, observed that the average error rate for HumanBoost was below the others. We therefore conclude that PTDs are available as new heuristics, and HumanBoost has the potential to improve detection accuracy for Web user.
Cost-sensitive AdaBoost Algorithm for Multi-class Classification Problems
多分类问题代价敏感AdaBoost算法

FU Zhong-Liang,
付忠良

自动化学报 , 2011,
Abstract: To solve the cost merging problem when multi-class cost-sensitive classification is transferred to two-class cost-sensitive classification, a cost-sensitive AdaBoost algorithm which can be applied directly to multi-class classification is constructed. The proposed algorithm is similar to real AdaBoost algorithm in algorithm flow and error estimation formula. When the costs are equal, this algorithm becomes a new real AdaBoost algorithm for multi-class classification, guaranteeing that the training error of the combination classifier could be reduced while the number of trained classifiers increased. The new real AdaBoost algorithm does not need to meet the condition that every classifier must be independent, that is to say, the independent condition of classifiers can be derived from the new algorithm, instead of being the must for current real AdaBoost algorithm for multi-class classification. The experimental results show that this new algorithm always ensures the classification result trends to the class with the smallest cost, while the existing multi-class cost-sensitive learning algorithm may fail if the costs of being erroneously classified to other classes are imbalanced and the average cost of every class is equal. The research method above provides a new idea to construct new ensemble learning algorithms, and an AdaBoost algorithm for multi-label classification is given, which is easy to operate and approximately meets the smallest error classification rate.
Digital Interactive Kanban Advertisement System Using Face Recognition Methodology  [PDF]
Feng-Yi Cheng, Chu-Ja Chang, Gwo-Jia Jong
Computational Water, Energy, and Environmental Engineering (CWEEE) , 2013, DOI: 10.4236/cweee.2013.23B005
Abstract:

Most of advertisement systems are presently still launch the publicity content by the static words and pictures. Recently, this static advertisement model will not be able to attract peoples attention more and more. Moreover, the static information content of advertisement system is limited because of the layout shown size. It can not also fully demonstrate the information content of advertisement system. In this paper, we develop a digital interactive kanban advertisement system using face recognition methodology to solve these problems. The system captures the persons face through the camera. The digital advertisement content size is relevant by the person and camera observation locations. In this paper, we adopt the Adaboost algorithm to judge people face, and the system only need to grab the position of the face. The system doesn’t built expensive and complex equipment to reduce the system cost and enhance the system performance. This system can also achieve the same similar digital interactive advertising effectiveness.

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