oalib

Publish in OALib Journal

ISSN: 2333-9721

APC: Only $99

Submit

Any time

2019 ( 152 )

2018 ( 1291 )

2017 ( 1300 )

2016 ( 1438 )

Custom range...

Search Results: 1 - 10 of 32288 matches for " Adaboost Multi-Expression Classification Algorithm "
All listed articles are free for downloading (OA Articles)
Page 1 /32288
Display every page Item
The Study of Multi-Expression Classification Algorithm Based on Adaboost and Mutual Independent Feature  [PDF]
Liying Lang, Zuntao Hu
Journal of Signal and Information Processing (JSIP) , 2011, DOI: 10.4236/jsip.2011.24038
Abstract: In the paper conventional Adaboost algorithm is improved and local features of face such as eyes and mouth are separated as mutual independent elements for facial feature extraction and classification. The multi-expression classification algorithm which is based on Adaboost and mutual independent feature is proposed. In order to effectively and quickly train threshold values of weak classifiers of features, Sample of training is carried out simple improvement. We obtain a good classification results through experiments.
基于标签相关性的多标签分类AdaBoost算法
王莉莉,付忠良
- , 2016, DOI: 10.15961/j.jsuese.2016.05.014
Abstract: 中文摘要: 在多标签分类问题中,标签之间往往是相关的,为了提高分类性能,利用标签之间的相关性,提出AdaBoost.MLR算法和标签相关性分析方法。AdaBoost.MLR算法采用余弦相似度来计算标签相关性矩阵,利用标签相关性矩阵对原始标签矩阵进行补全转换为模糊标签矩阵,将标签空间划分为标签集、标签相关集和标签关集,结合标签之间的相关性和弱分类器的分类情况,对样本权重进行调整。AdaBoost.MLR算法也能解决多类别分类问题,在其标签相关性的计算中,根据已经训练的弱分类器得到的临时强分类器的分类结果,构造标签相似性矩阵。实验结果表明,文中提出的算法在实验数据集上优于现有的算法,尤其在标签相关性复杂的数据集上分类性能有显著提升。
Abstract:In order to improve classification performance and exploit label correlations,AdaBoost.MLR algorithm was proposed.Cosine similarity was adopted to capture the complex correlations among labels in AdaBoost.MLR algorithm,a supplementary label matrix was incorporated,which augments the incomplete original label matrix by exploiting the label correlations,label space was divided into three parts of label set,relevant label set and irrelevant label set,weight update rule was modified according to correlations among labels and the results of weak learner.AdaBoost.MLR algorithm was able to solve multi class classification problem specially,label similarity matrix,instead of cosine similarity,was constructed by the classification results of temporary strong learner combined by previous trained weak learners.The experimental results illustrated that the proposed algorithm was superior to existing algorithms,and the classification performance was improved significantly on datasets had complex correlations among labels.
多标签AdaBoost算法的改进算法
付忠良,张丹普,王莉莉
- , 2015, DOI: 10.15961/j.jsuese.2015.05.015
Abstract: 中文摘要: 针对多标签AdaBoost系列算法,以尽量减小算法的学习错误率为目的,提出了对其进行改进的2种思路。基于改进思路构造出了改进的多标签AdaBoost算法。一种思路是修改算法的样本分布调整策略,破坏现有AdaBoost算法中样本分布的均匀性,以确保增加每一个弱分类器都能降低学习错误的上界估计,从而实现对多标签AdaBoost算法的改进;另一种思路是训练弱分类器时兼顾后续待学习的弱分类器对学习错误的影响,克服现有算法在训练弱分类器时只考虑当前弱分类器对学习错误的影响,而完全忽略后续待学习的弱分类器对学习错误的影响这一现象,从而改进多标签AdaBoost算法。理论上,对于改进多标签AdaBoost算法,增加每一个弱分类器都能进一步降低学习错误。理论分析和实验结果均表明了提出的改进算法有改进效果。
Abstract:Aiming to decrease the learning error of the series of AdaBoost algorithm for multi-label classification,the AdaBoost algorithm was improved for multi-label classification by two strategies.One idea is to modify the adjustment strategy of sample distribution,and destroy the sample uniform distribution in the existing AdaBoost algorithm,in order to ensure that the increase of every weak classifier can reduce the learning error bound estimation.Another idea is to consider the effect of subsequent weak classifiers to decrease the learning error when training current weak classifier,which is different from the existing AdaBoost algorithm.Theoretically,the improved AdaBoost algorithms for multi-label classification increase every weak classifier to reduce more learning error.Theoretical analysis and experimental results showed that all the improved algorithms are effective.
结合多特征融合算法的人数统计与分析系统
People Counting System Combined with Multi-Feature Fusion Algorithm
 [PDF]

李宏广, 范红, 许武军
Journal of Image and Signal Processing (JISP) , 2016, DOI: 10.12677/JISP.2016.53013
Abstract:
为了实现高准确率的实时人数统计,设计了嵌入式片上系统平台。该系统采用交叉编译的方式,对人体目标进行实时检测与跟踪,实现了对指定区域内的人数统计与分析。本系统利用了AdaBoost分类器对图像/视频中目标进行人脸识别,基于Haar特征提取进行目标跟踪。提出结合多特征融合算法以及人脸识别参数自适应算法,在Xilinx公司的Zynq-7000开发板上实现了目标实时性和快速性的统计与分析。结果表明:该系统充分节省了嵌入式平台的资源,简化了整个系统的开发流程,并提高了系统的兼容性和可移植性。结合多特征的融合算法使人数统计的准确率高达97%以上。该系统具有安装位置灵活,实时性好、准确率高、稳定性好等特点。
In order to achieve high accuracy people counting, the embedded platform is designed in this pa-per. The system realizes real-time target detection and tracking using the cross compiler, to achieve the number of people within the designated area. The system uses the AdaBoost classifier image/video target recognition and feature extraction based on Haar for target tracking. The combination of multi-feature fusion algorithm and face recognition parameters adaptive algorithms is proposed in Xilinx’s Zynq-7000 development board to achieve the goal of real-time and fast statistics and analysis. The results show that: The system saves resources of embedded platforms and simplifies the entire system development process, improving the whole compatibility and portability. It can realize people counting and analysis in the image/video with accuracy rate of more than 97%. Basically it meets the requirements of human counting in traffic statistics: Parallel processing, high accuracy, stable and reliable, etc.
Advance Ensemble Learning of Fuzzy Classification Rules Based on AdaBoost
基于AdaBoost的改进模糊分类规则集成学习

Fang Min,Wang Bao-shu,
方敏
,王宝树

电子与信息学报 , 2005,
Abstract: A new learning algorithm of fuzzy classification rules is presented based on ensemble learning algorithm. By tuning the distribution of training instances during each AdaBoost iterative training, the classification rules with fuzzy antecedent and consequent are produced with genetic algorithm. The distribution of training instances participate in computing of the fitness function and the collaboration of rules which are complementary is taken into account during rules producing, so that the classification error rate is reduced and performance of the classification based on the fuzzy rules is improved.
Multi Stage Filter Using Enhanced Adaboost for Network Intrusion Detection
P.Natesan,P.Balasubramanie
International Journal of Network Security & Its Applications , 2012,
Abstract: Based on the analysis and distribution of network attacks in KDDCup99 dataset and real time traffic, this paper proposes a design of multi stage filter which is an efficient and effective approach in dealing with various categories of attacks in networks. The first stage of the filter is designed using Enhanced Adaboost with Decision tree algorithm to detect the frequent attacks occurs in the network and the second stage of the filter is designed using enhanced Adaboost with Na ve Byes algorithm to detectthe moderate attacks occurs in the network. The final stage of the filter is used to detect the infrequent attack which is designed using the enhanced Adaboost algorithm with Na ve Bayes as a base learner. Performance of this design is tested with the KDDCup99 dataset and is shown to have high detection rate with low false alarm rates.
Adaboost Ensemble with Genetic Algorithm Post Optimization for Intrusion Detection
Hany M. Harb,Abeer S. Desuky
International Journal of Computer Science Issues , 2011,
Abstract: This paper presents a fast learning algorithm using Adaboost ensemble with simple genetic algorithms (GAs) for intrusion detection systems. Unlike traditional approaches using Adaboost algorithms, it proposed a Genetic Algorithm post optimization procedure for the found classifiers and their coefficients removing the redundancy classifiers which cause higher error rates and leading to shorter final classifiers and a speedup of classification. This approach has been implemented and tested on the NSL-KDD dataset and its experimental results show that the method reduces the complexity of computation, while maintaining the high detection accuracy. Moreover, the method improves the processing time, so it is especially appealing for the real-time processing of the intrusion detection system.
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.
Network traffic classification based on GA-CFS and AdaBoost algorithm
基于GA-CFS和AdaBoost算法的网络流量分类

LA Ting-ting,SHI Jun,
剌婷婷
,师 军

计算机应用研究 , 2012,
Abstract: The selection of feature attribute plays an important role in the network traffic classification. This paper applied a method considering the CFS algorithm as the fitness function of the improved genetic algorithm GA-CFS in order to extract the main flow statistical attributes in the space of 249 attributes and selected 18 attributes of a flow as the best feature subset. Finally it used the AdaBoost algorithm to enhance a series of weak classifiers to the strong classifiers. At the same time, it fulfilled the classification of the network traffic, and further studied the network traffic intensively. The experimental results indicate that GA-CFS and AdaBoost algorithm can achieve higher classification precision compared with the weak classifiers.
Geometric Feature-Based Facial Expression Recognition in Image Sequences Using Multi-Class AdaBoost and Support Vector Machines
Deepak Ghimire,Joonwhoan Lee
Sensors , 2013, DOI: 10.3390/s130607714
Abstract: Facial expressions are widely used in the behavioral interpretation of emotions, cognitive science, and social interactions. In this paper, we present a novel method for fully automatic facial expression recognition in facial image sequences. As the facial expression evolves over time facial landmarks are automatically tracked in consecutive video frames, using displacements based on elastic bunch graph matching displacement estimation. Feature vectors from individual landmarks, as well as pairs of landmarks tracking results are extracted, and normalized, with respect to the first frame in the sequence. The prototypical expression sequence for each class of facial expression is formed, by taking the median of the landmark tracking results from the training facial expression sequences. Multi-class AdaBoost with dynamic time warping similarity distance between the feature vector of input facial expression and prototypical facial expression, is used as a weak classifier to select the subset of discriminative feature vectors. Finally, two methods for facial expression recognition are presented, either by using multi-class AdaBoost with dynamic time warping, or by using support vector machine on the boosted feature vectors. The results on the Cohn-Kanade (CK+) facial expression database show a recognition accuracy of 95.17% and 97.35% using multi-class AdaBoost and support vector machines, respectively.
Page 1 /32288
Display every page Item


Home
Copyright © 2008-2017 Open Access Library. All rights reserved.