基于码本模型和多特征的早期烟雾检测

提出一种基于码本模型和多特征的早期烟雾检测算法。首先,利用码本模型进行前景提取;然后,结合烟雾的颜色模型和形状特征模型检测前景中的疑似烟雾区域;最后,利用烟雾的动态特性,有效地降低误检率,提高算法的鲁棒性。通过ROC曲线对比,实验结果表明,该算法具有良好的烟雾检测能力。同时,能够满足实时性要求,具有较高的实用价值。     

异步脑—机接口的空闲状态检测新方法*

提出一种新的空闲状态检测方法,以训练集中各类运动想象样本的类内散度和正确检测率为指标,结合接收机曲线确定分类阈值,设计最佳三分类器,并采用模糊化技术对预测标签进行处理。将该方法应用于2005年BCI竞赛数据IVc,测试的均方误差为0.278 7。实验结果验证了该方法的有效性。     

基于ROC曲线的镜片舒适度眼动指标研究

基于眼动仪检测到的受试者眼动数据,提出一项新的眼动指标——眼睑间距变动百分比,并对比传统的眼跳次数、瞳孔直径等眼动指标,经过显著性和相关性分析后,最后利用受试者特征曲线（Receiver operating characteristic,ROC）曲线评价每个眼动指标对镜片舒适度的判别性能。实验结果表明,本文提出的眼睑间距变动百分比反映镜片舒适度的性能最好,瞳孔直径次之,眼跳次数和平均注视时长相仿。本实验结果证明可以通过检测患者的眼睑间距变动百分比,来准确快速地判断镜片的舒适程度。这项研究成果能帮助相关从业者从更加客观的角度评价镜片的舒适程度,也为中国当前日益严峻的近视防控形势提供了一定的技术理论帮助。     

Logistic回归与分类树模型的比较

为了有效地评估客户的可信度,提高信贷机构经济效益。文中通过统计学中的参数方法Logistic回归和非参数方法分类树这两种方法,建立两种模型对数据进行预测,应用SPSS软件的Binary Logistic Regression方法,利用ROC曲线的性质来对模型的性能进行评价,根据输出结果比较两种模型在应用中都是可行有效的,在实际操作中应因地制宜,把握两种方法的优势,得到更有价值的结果.     

Improvement of SiC_p Reinforced Cast AluminiumComposites by the ROC Method

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基于ROC曲线寻优的支持向量机性能研究

支持向量机在小样本模式识别领域具有优势,但其性能评估及核参数、正则化参数的选择尚未有标准算法.将受试者操作特性曲线(Receiver Operating Characteristic,ROC)引入支持向量机分类性能分析和建模参数优化问题.在核参数及正则化参数所构成的二维空间中,调整模型参数阈值描绘ROC曲线,通过比较不同分类器ROC曲线下面积实现模型的性能分析,研究了基于ROC曲线最佳工作点的模型优化问题.工程实例表明,ROC曲线下面积有效地量化了模型的识别性能,并给出了一定寻优范围内的模型参数最优点,可以在SVM模型参数优化问题中推广应用.     

Instance weighting versus threshold adjusting for cost-sensitive classification

In real-world classification problems, different types of misclassification errors often have asymmetric costs, thus demanding cost-sensitive learning methods that attempt to minimize average misclassification cost rather than plain error rate. Instance weighting and post hoc threshold adjusting are two major approaches to cost-sensitive classifier learning. This paper compares the effects of these two approaches on several standard, off-the-shelf classification methods. The comparison indicates that the two approaches lead to similar results for some classification methods, such as Naïve Bayes, logistic regression, and backpropagation neural network, but very different results for other methods, such as decision tree, decision table, and decision rule learners. The findings from this research have important implications on the selection of the cost-sensitive classifier learning approach as well as on the interpretation of a recently published finding about the relative performance of Naïve Bayes and decision trees.     

软件能力可信研究综述

能力可信是可信计算研究领域中的重要研究内容,它要求系统的功能是可信赖的,如今,系统的开发方式,系统规模、应用模式已发生巨大变化,这使得获得功能的可信赖性的任务更具挑战性,首先综述能力可信的概念、内涵等,详细介绍了能力可信的性能指标,从缺陷分析、可靠性模型、恢复技术三个方面讨论了能力可信的研究现状、和存在问题,最后对能力可信的未来发展趋势进行了探讨.     

基于已知特征项和环境相关量的特征提取算法

在现有基于已知特征项特征提取算法的基础上,提出一种基于已知特征项和环境相关量的特征提取算法。该算法通过已知特征项搜索频繁项集,提高了特征提取速度。环境相关量的引入使提取的特征能更准确地描述入侵行为。实验结果表明,该算法在提取同类入侵行为的特征时,比Signature Apriori算法更快更准确。     

Exploiting the relationships among several binary classifiers via data transformation

The structural resemblance among several existing classifiers has motivated us to investigate their underlying relationships. By exploring into the mapping solutions of these classifiers, we found that they can be linked by simple feature data scaling. In other words, the key to these relationships lies upon how the replica of feature data are being scaled. This finding leads us directly to an exploration of novel classifiers beyond existing settings. Based on an extensive empirical evaluation, we show that the proposed formulation facilitates a tuning capability beyond existing settings for classifier generalization.