基于约束最小二乘的光学遥感图像融合

在光学遥感图像融合方法中,最小二乘法常被用于求解多光谱图像拟合低分辨率全色图像的线性回归系数,但是回归系数常常出现负数,导致其物理意义不明确。针对这种实际情况,提出了基于约束最小二乘的低分辨率全色图像构造方法。通过IKONOS-2全色与多光谱图像的融合实验,结果表明：该方法所求得的回归系数具有明确物理意义,符合实际情况,并且与光谱响应函数法、最小二乘法相比,其融合质量基本保持一致,并且由于该方法不需要先验知识,故其实用性较强。     

面向属性效应控制的等均值约束支持向量回归机

针对现有的属性效应控制方法无法有效控制非线性回归建模的属性效应问题, 基于间隔最大化和结构风险最小化原则, 通过向SVR 目标学习准则中施加等均值条件约束, 提出等均值支持向量回归机(EM-SVR). 所提出的方法具有较好的泛化能力, 同时继承了EM-LS 的良好性能. 实验结果验证了所提出方法的有效性.

     

VANET中基于代理与回归聚集的低延迟紧急数据处理算法*

车载自组织网络(VANET)中有关安全的信息无疑具有最高优先级,应为其设计低延迟、高准确率的信息传递算法,基于此,提出一种基于认知代理的紧急数据处理算法。为每个簇分配三种代理,数据采集代理与数据传递代理独立、并行的工作,提高了整体处理的效率。紧急数据的聚集过程中,基于回归模型,过滤其中的冗余信息与重复信息,提高了数据紧凑度。试验结果表明,相较于其他仅针对数据传输的VANET路由算法,本算法具有更低的端到端延迟与控制信息的开销。     

基于RS-SVR的企业信用评分模型

准确的信用评分模型可以提升银行的决策能力,将支持向量回归模型应用于企业信用评分问题,并提出基于随机子集的支持向量回归集成模型。首先使用随机子集抽样模型获得足够多不同的训练数据集,接下来使用不同的训练集子集训练得到不同的支持向量回归模型,最后使用简单平均方法整合不同模型的预测结果,在企业信用评分数据上的实验结果证明了模型的有效性。     

基于多元线性回归的雾霾预测方法研究

提出了一种在线样本更新的多元线性回归分析的雾霾预测方法。首先搜集了北京市天气状况,包括平均气温、湿度、风级等气象数据以及PM2.5、CO、NO₂、SO₂等大气成分浓度数据,然后通过散点图对这些因素进行主要影响因素分析,筛选出对雾霾影响比较明显的因素作为雾霾预测的依据。通过在线样本更新的多元线性回归建立了PM2.5含量预测模型,并将气象要素作为雾霾的判断标准。最后给出实际例子,利用多元线性回归对北京未来一天、三天及一周的PM2.5含量进行较为精确的预测。     

Ordinal regression by a gravitational model in the field of educational data mining

Educational data mining (EDM) is a research area where the goal is to develop data mining methods to examine data critically from educational environments. Traditionally, EDM has addressed the following problems: clustering, classification, regression, anomaly detection and association rule mining. In this paper, the ordinal regression (OR) paradigm, is introduced in the field of EDM. The goal of OR problems is the classification of items in an ordinal scale. For instance, the prediction of students' performance in categories (where the different grades could be ordered according to A ? B ? C ? D) is a classical example of an OR problem. The EDM community has not yet explored this paradigm (despite the importance of these problems in the field of EDM). Furthermore, an amenable and interpretable OR model based on the concept of gravitation is proposed. The model is an extension of a recently proposed gravitational model that tackles imbalanced nominal classification problems. The model is carefully adapted to the ordinal scenario and validated with four EDM datasets. The results obtained were compared with state‐of‐the‐art OR algorithms and nominal classification ones. The proposed models can be used to better understand the learning–teaching process in higher education environments.     

Optimal back-off point determination and controller weight selection for multivariate systems under finite-horizon control

Plant economic performance is most often related to the operating point, specifically the mean values of the process variables; meanwhile, most existing performance assessment techniques involve examining the variances or covariances of the controlled variables. A combined approach is to determine the appropriate trade-off between variances of different process variables in order to operate the plant at the point that provides maximum economic benefit while satisfying the operating constraints. This problem is referred to as the minimum backed-off operating point selection, and previous works have formulated it as a non-convex constrained optimization problem. In the current work, a new technique is introduced that can provide the optimal plant operating point. Additionally, this method provides the weights for a finite horizon controller that results in the optimal trade-off in process variable variances that will allow satisfaction of the operating constraints at the optimal operating point. In this method, the plant and disturbance models for the given process are used to generate data representing possible trade-offs between process variable standard deviations. Employing a piecewise linear regression to describe the sample points of this standard deviations data allows for the operating point selection problem to be solved as a small number of linear programs. The advantages of this approach are demonstrated through the use of mathematical and simulation case studies.     

高校学生恋爱影响因素的累积Logistic回归分析模型

高校学生恋爱对其自身学习与成长有很大的影响,为了帮助大学生树立积极向上的恋爱观。本文通过对我国10所高校500份“高校学生恋爱调查报告”所获数据的Pearson相关性分析,引入恋爱疏远度来表示恋爱双方的感情程度。在分析各单因素对恋爱疏远度影响的基础上,建立综合因素下高校学生恋爱影响因素的累积Logistic回归分析模型,并通过模型确定出影响高校学生恋爱的两个重要因素。     

A service recovery method to enhance customer satisfaction with a case study of motion sport game

Service recovery is a concept that involves regaining customer satisfaction and loyalty. Although existing studies have addressed its importance, few service recovery methodologies have been developed. Thus, there is a need to develop a systematic framework that can first detect customer status and then provide appropriate recovery recommendations to ensure customer satisfaction. This study develops such a framework in relation to an individual health maintenance practice. While regular moderate exercise is known to promote good health and help prevent disease, the lack of exercise remains a crucial health issue. This study presents a logistic regression model that uses both physiological data (heart rate and blood pressure) and psychological data (ratings of perceived exertion) to detect the effectiveness of an individual's exercise and to provide sports‐related service recovery suggestions as needed. To implement and assess this service recovery model, the use of a motion gaming system is proposed.     

基于LASSO回归模型的网贷借款成功影响因素挖掘

随着P2P网络借贷交易量的增大,对P2P交易数据的挖掘和分析备受关注,其中一项重要的研究课题是网络借款成功率的影响因素分析.现有的文献多采用线性回归方法对该课题进行研究,但未考虑变量之间的多重共线性和采用最优变量子集建立回归模型的问题.本文采用Lasso回归方法,建立最优变量子集的回归模型对影响网络借款成功率的因素进行分析,避免了多重共线性问题对模型的干扰,同时提高了模型对数据的拟合精度.对Lending Club平台的借贷数据的实证分析结果显示,本文方法在模型的拟合精度和避免共线性方面优于对比方法.