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1.
基于多分类器融合算法的3D人脸年龄识别   总被引:2,自引:0,他引:2  
为了提高人脸识别中待测人脸图像年龄估计的正确率,提出了一种基于多分类器融合的3D人脸年龄识别算法.首先.利用人脸的纹理信息将二维图像映射到标准三维模型上,并以贝叶斯决策理论为基础,对Kittler提出的多分类器融合算法理论框架及其组合规则进行了详细的研究、讨论和改进,然后应用改进后的多分类器组合规则将多个单独识别分类器加以融合以达到分类未知年龄目标人脸的目的,并估计人脸年龄.实验结果表明,算法可有效估计日标人脸年龄,并减小估计误差.  相似文献
2.
基于粗糙集约简的多分类器系统构造方法   总被引:1,自引:0,他引:1       下载免费PDF全文
多分类器系统是近年来兴起的一种有效的分类机制,为提高多分类器系统的分类精度,提出了一种基于粗糙集约简构造多分类器系统的机制,并从输入和输出两个角度对如何选择单个分类器进行了探讨。通过对4个UCI数据集进行验证,发现基于输出的选择融合方法得到了最好的分类效果。  相似文献
3.
Accurate estimation of class membership probability is needed for many applications in data mining and decision-making, to which multiclass classification is often applied. Since existing methods for estimation of class membership probability are designed for binary classification, in which only a single score outputted from a classifier can be used, an approach for multiclass classification requires both a decomposition of a multiclass classifier into binary classifiers and a combination of estimates obtained from each binary classifier to a target estimate. We propose a simple and general method for directly estimating class membership probability for any class in multiclass classification without decomposition and combination, using multiple scores not only for a predicted class but also for other proper classes. To make it possible to use multiple scores, we propose to modify or extend representative existing methods. As a non-parametric method, which refers to the idea of a binning method as proposed by Zadrozny et al., we create an “accuracy table” by a different method. Moreover we smooth accuracies on the table with methods such as the moving average to yield reliable probabilities (accuracies). As a parametric method, we extend Platt’s method to apply a multiple logistic regression. On two different datasets (open-ended data from Japanese social surveys and the 20 Newsgroups) both with Support Vector Machines and naive Bayes classifiers, we empirically show that the use of multiple scores is effective in the estimation of class membership probabilities in multiclass classification in terms of cross entropy, the reliability diagram, the ROC curve and AUC (area under the ROC curve), and that the proposed smoothing method for the accuracy table works quite well. Finally, we show empirically that in terms of MSE (mean squared error), our best proposed method is superior to an expansion for multiclass classification of a PAV method proposed by Zadrozny et al., in both the 20 Newsgroups dataset and the Pendigits dataset, but is slightly worse than the state-of-the-art method, which is an expansion for multiclass classification of a combination of boosting and a PAV method, on the Pendigits dataset.
Manabu OkumuraEmail:
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4.
神经网络集成和支持向量机都是在机器学习领域很流行的方法。集成方法成功地提高了神经网络的稳健性和精度,其中选择性集成方法通过算法选择差异度大的个体,取得了很好的效果。而支持向量机更是克服了神经网络的局部最优,不稳定等缺点,也在多个方面取得了很好的结果。该文着重研究这两种方法在小样本多类数据集上的性能,在四个真实数据集上的结果表明,支持向量机性能要比神经网络集成稍好.  相似文献
5.
为提高供应链物流管理服务水平,基于帕累托定律,运用规范列平均法和优化理论建立了基于多重分类准则模型。通过有效利用混沌遗传和蚁群优化算法在组合优化中的优势,给出了混沌遗传蚁群优化算法,采用混沌搜索优化初始群体、修正变异算子、蚁群算法寻优优化、改进相关参数等实现了两种算法的有机集成。物流案例实证表明了混沌遗传蚁群算法在解决多重分类准则优化模型方面的有效性。  相似文献
6.
Expert systems have traditionally captured the explicit knowledge of a single expert or source of expertise in order to automatically provide conclusions or classifications within a narrow problem domain. This is in stark contrast to social software which enables knowledge communities to share implicit knowledge of a more practical or experiential nature to inform individuals and groups to arrive at their own conclusions. Specialists are often needed to elicit and encode the knowledge in the case of expert systems, whereas one of the (claimed) hallmarks of social software and the Web 2.0 trend, such as Wikis and Blogs, is that everyone, anywhere can chose to contribute input. This openness in authoring and sharing content, however, tends to produce unstructured knowledge that is difficult to execute, reason over or automatically validate. This also poses limitations for its reuse. To facilitate the capture of knowledge-in-action which spans both explicit and tacit knowledge types, a knowledge engineering approach which offers Wiki-style collaboration is introduced. The approach extends a combined rule and case-based knowledge acquisition technique known as Multiple Classification Ripple Down Rules to allow multiple users to collaboratively view, define and refine a knowledge base over time and space.  相似文献
7.
目的 图文数据在不同应用场景下的最佳分类方法各不相同,而现有语义级融合算法大多适用于图文数据分类方法相同的情况,若将其应用于不同分类方法时由于分类决策基准不统一导致分类结果不理想,大幅降低了融合分类性能。针对这一问题,提出基于加权KNN的融合分类方法。方法 首先,分别利用softmax多分类器和多分类支持向量机(SVM)实现图像和文本分类,同时利用训练数据集各类别分类精确度加权后的图像和文本正确判别实例的分类决策值分别构建图像和文本KNN模型;再分别利用其对测试实例的图像和文本分类决策值进行预测,通过最邻近k个实例属于各类别的数目确定测试实例的分类概率,统一图像和文本的分类决策基准;最后利用训练数据集中图像和文本分类正确的数目确定测试实例中图像和文本分类概率的融合系数,实现统一分类决策基准下的图文数据融合。结果 在Attribute Discovery数据集的图像文本对上进行实验,并与基准方法进行比较,实验结果表明,本文融合算法的分类精确度高于图像和文本各自的分类精确度,且平均分类精确度相比基准方法提高了4.45%;此外,本文算法对图文信息的平均整合能力相比基准方法提高了4.19%。结论 本文算法将图像和文本不同分类方法的分类决策基准统一化,实现了图文数据的有效融合,具有较强的信息整合能力和较好的融合分类性能。  相似文献
8.
针对目前机械故障诊断中难以进行特征提取和常规SVM算法诊断多类分类问题时存在困难等问题,提出了结合了WPA理论和基于二叉树的多级SVM分类器的WPA-SVM多分类故障混合诊断模型。采用小波包分析对机械信号提取频域能量特征向量,通过训练多个依赖故障优先级的基于二叉树的多级SVM分类器中,找到样本中的支持向量,并以此决定超平面。然后根据最优分类平面,对测试集的样本进行故障诊断。通过对两种不同特征提取方法、三种不同SVM识别策略的实验比较结果可知,该方法是有效的。  相似文献
9.
A dynamic classification using the support vector machine (SVM) technique is presented in this paper as a new ‘incremental’ framework for multiple-classifying video stream data. The contribution of this study is the derivation of a unique, fast and simple to implement technique that allows multi-classification of behavioral motions based on an adaptation of the least-square SVM (LS-SVM) formulation. This dynamic approach leads to an extension of SVM beyond its current static image-based learning capabilities. The proposed incremental multi-classification method is applied to video stream data, which consists of an articulated humanoid model monitored by a surveillance camera. The initial supervised off-line learning phase is followed by a visual behavior data acquisition and then an incremental learning phase. The resulting error rate and the confidence level for the proposed technique demonstrate its validity and merits in articulated motion learning. Furthermore, the enabled online learning allows an adaptive domain knowledge insertion and provides the advantage of reducing both the model training time and the information storage requirements of the overall system which are both essential for dynamic soft computing applications.  相似文献
10.
本文针对数据库管理系统中缓冲区的特殊重要地位,介绍了多缓冲池的配置及自调优的概念。将缓冲区分为数个独立的缓冲池,为不同性质的数据库对象分别建立独立的缓冲池,也为不同的负载提供最佳的配置方式,能够减少不同负载下的i/os时间,提高吞吐率。本文介绍了多缓冲池配置的模型,并基于此模型提出了多缓冲池的多次划分自调优算法。  相似文献
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