一种新的基于SVM参数优化的算法

针对单纯使用遗传算法处理大规模数据需要时间长和对计算机的内存等硬件要求较高的问题,将神经网络嵌入到遗传算法中构造出混合智能遗传算法用于SVM核函数的参数优化,数值试验结果表明该算法对SVM核参数优化是可行的、有效的,并能得到较好的SVM核参数组合和具有较高的分类准确率及较好的泛化能力.     

基于SVM的车牌数字识别方法

运用支持向量机对车牌字符进行识别,解决了由于图像受客观条件的影响、样本数量不是很大等原因导致的识别率不高的问题.主要针对车牌字符中的数字进行实验,选取了15组数字样本,8组进行训练,7组进行测试,采用交叉验证的思想对SVM进行参数C与g的寻优,并选择合适的核函数,对样本进行训练和预测,对于某些数字的识别率可达到100%,并在相同的训练集和测试集下与BP网络的识别效果进行对比.实验结果表明,SVM在训练样本较少且无字符特征提取的情况下具有很好的识别率,并且有很好的分类推广能力.     

基于KMOD核函数的SVM方法在信用评分中的应用

本文介绍了支持向量分类机,并引入具有更好识别能力的KMOD核函数建立了SVM信用卡分类模型.利用澳大利亚和德国的信用卡数据进行了数值实验,结果表明该模型在分类准确率、支持向量方面优于基于RBF的SVM模型.     

小邻域方法对数据的分类

为了由数据特征实施对数据更好的查询、图像更好的分割,提出了一种小邻域分类(ε-NC)方法.ε-NC能找出最佳的分类数量,分类时间明显减少.引入相对熵损失来限制样本图像的失真,采用可变步长搜索方法提高最小采样率搜索过程的效率;给出模糊隶属度,形成分类的有效性判断函数,减少迭代次数.采用ε-NC分类方法,对图像分割的准确率高、速度快、抗噪能力强,它对图像目标分类的准确率比传统最好的分类方法的准确率平均提高了5.65%,搜索速度提高9-12倍.     

DNA分类模型

本模型充分利用了所给数据的特点 ,运用统计、最优化等数学方法 ,从已知样本序列中提炼出能较好代表两类特征的关键字符串 ,据此提出量化的分类标准 ,能较好的对任给 DNA序列进行分类 .首先 ,从已知样本序列中用广度优先法选出所有重复出现的字符串 ,并计算其标准化频率及分散度 .然后 ,利用样本数据结合最小二乘法确定两类字符串各自的优先级函数 ,并且逐步优化其参数使之达到稳定 ,提高了可信度 .最后 ,根据优先级函数找出关键词 ,然后确定权数 ,用层次分析法对未知样本进行分类 ,并定出显著水平 ,从而得到了一个比较通用的分类方法 .经过检验 ,此方法对 2 1— 4 0号待测样本进行了很好的分类 ,对后面的1 82个 DNA序列进行同样的操作 ,也有较好的效果     

数据缺失情况下基于支持向量机的心脏病诊断

为在数据缺失的情况下进行心脏病诊断并获得较高的准确率,对缺失值进行处理后,利用径向基函数支持向量机,采用交叉验证和网格搜索寻找最佳惩罚参数和关联参数,对UCI Heart数据集进行分类,多分类准确率为81.89%,二分类准确率为89.61%.仿真结果表明,支持向量机网络模型性能稳定,样本追加能力强,训练时间短,分类效果好,在心脏病等医疗诊断中有很大的应用潜力.     

一种加权的支持向量机及其在储层识别中的应用

标准支持向量机(SVM)抗噪声能力不强,当训练样本中存在有噪声或者野点时,会影响最优分类面的产生,最终导致分类结果出现偏差。针对这一问题,提出了一种考虑最小包围球的加权支持向量机(WSVM),给每个样本点赋予不同的权值,以此来降低噪声或野点对分类结果的影响。对江汉油田某区块的oilsk81,oilsk83和oilsk85三口油井的测井数据进行交叉验证,其中核函数采用了线性、指数和RBF这3种不同的核函数。测试结果显示,无论是在SVM还是在WSVM中,核函数选择RBF识别率都是最高的,同时提出的WSVM不受核函数的影响,识别稳定性好,且在交叉验证中识别率都能够达到100%。     

基于高斯混合和BP神经网络的卵巢癌质谱数据三分类模型

癌症的早期诊断可以显著提高癌症患者的存活率,三分类问题就是将未知样本与已知样本进行匹配度检测,预测样本是健康状态,良性发展状态,还是癌症状态.针对复杂难分的卵巢癌蛋白质质谱数据,提出了一种基于高斯混合模型和BP神经网络的三分类预测模型.首先,去除原数据中的冗余,对其进行方差排序及交集筛选提取特征集合一,再利用高斯混合模型处理求得参数作为特征集合二,最后使用BP神经网络进行样本三分类,准确率达到72.9%.结果表明:模型可以作为卵巢癌质谱数据三分类的可选择工具.     

基于PCA和改进的PSO-SVM的机器人移动方向分类

随着人们创新水平的不断提高,为了更加准确的实现机器人的导航任务,提出了一种基于改进的粒子群优化支持向量机中的参数的方法.首先利用主成分分析法对数据进行降维,然后利用改进的粒子群优化算法,对SVM中的惩罚参数c和核函数的参数g进行优化,最后代入到SVM中,以此来达到运用SVM对机器人的导航任务进行分类识别.相对于其他算法,容易发现改进的粒子群优化算法优化后的支持向量机可以达到很好的效果.这种识别分类可以帮助人们很好的对机器人进行导航,对今后机器人的研究具有很大的应用价值.     

Logistic回归模型在信用风险分析中的应用

通过运行SPSS,建立L og istic回归信用评价模型(cred it eva luation m odel),用来对中国2000年106家上市公司进行两类模式分类,这两类模式是指按照公司的经营状况分为“差”和“正常”两个小组.对每一家上市公司,考虑其经营状况的4个主要财务指标:每股收益、每股净资产、净资产收益率和每股现金流量.仿真结果表明,L og istic回归信用评价模型对总体106个样本,判别准确率达到99.06%.此外,本文的研究结果还发现,当利用SPSS的D iscrim inan t给出的模型系数建立的线性判别分析模型和利用SPSS的M u ltinom ia lL og istic给出的模型参数建立的L og istic回归模型,L og istic回归模型的判别结果不如线性判别模型.但如果剔除不合格的样本,或是将样本数据规格化,则可以提高L og istic回归模型的分类准确率.     

Del Pezzo orders on projective surfaces

In this paper, we generalise the notion of del Pezzo surfaces to orders on surfaces. We show that these del Pezzo orders have del Pezzo centre if the centre is normal Gorenstein and the order has finite representation type. We proceed to classify these del Pezzo orders. The main result is that if the centre is not or the quadric cone, then these del Pezzo orders can be obtained from del Pezzo orders on . Finally, we classify del Pezzo orders on and the quadric cone.     

Admission and pricing optimization of on-street parking with delivery bays

We analyze a parking lot, modeled as a loss queue, with passenger and delivery vehicles. The arrival process of delivery vehicles is exogenous, while that of passenger vehicles is a function of the parking price rate and accessibility. The aim of the parking operator is to maximize the revenue generated from passenger vehicles while providing a sufficient service level for delivery vehicles, in terms of their probability to find an available parking spot. Two levels of control are exercised: pricing and admission. From a Markov decision process approach, we prove that the optimal policy is a state-dependent reservation threshold policy that randomizes in at most one state. When some parking spots should be reserved for delivery vehicles, the price rate is selected to saturate the service level constraint, whereas when it is optimal not to restrict the parking lot accessibility, the price can also be selected as the unique local maximum of the revenue or to incentivize all potential passenger vehicles to arrive. Pricing should be used as a primary tool to control the flow of passenger vehicles. In complement, admission control is exercised with a limited use of reservation only when the service level guarantee for delivery vehicles is high.     

基于近邻传播聚类的地铁运营时段划分

地铁在每天不同时段客流量差异较大,运营时段的科学划分,是低峰与高峰列车运行计划合理交替的前提。目前地铁运营时段划分主要依据人工经验,主观性强且难以保证精度。以10min为时间间隔,把全天运营时间6∶00～23∶00分为102个时间点样本,将地铁沿线各车站每个时间点的进站客流量作为样本描述变量。采用近邻传播聚类算法将各时间点归并为不同类别,并引入CH、Hart以及IGP等聚类有效性评估指标对聚类结果加以检验以确定最优类别数,从而得到运营时段的最优划分方案和最佳时段分割点。天津地铁二号线实例研究表明,基于近邻传播聚类算法得到的运营时段划分结果更能体现实际客流需求波动特性,在此基础上优化行车计划后,旅客等待时间明显下降。     

Steady state results for the M/M(a,b)/c batch-service system

The transportation system considered in this paper has a number of vehicles with capacity constraint, which take passengers from a source terminal to various destinations and return to the terminal. The trip times are considered to be independent and identically distributed random variables with a common exponential distribution. Passengers arrive at the terminal in accordance with a Poisson process. The system is operated under the following policy: when a vehicle is available and there are at least ‘a’ passengers waiting for service, then a vehicle is dispatched immediately. A recursive algorithm is derived to obtain the steady-state probability P(m, j) that there are m idle vehicles and j waiting passengers in the queue. Analytical expressions have been derived for passenger queue length distribution, average passenger queue length, the r-th moment of passenger waiting time in the queue, service batch size distribution and the average service batch size, all in terms of P(0,0).     

支持向量机回归方法在地表水水质评价中的应用

将支持向量机方法应用于地表水质评价问题中,建立了多指标水质综合评价的支持向量机回归模型.在地表水质评价标准的基础上采用内插法获得学习样本,经过训练,得到水质评价的分类区间;然后以实测资料对所建模型进行检验,研究结果表明,支持向量机回归模型性能良好、预测精度高、简便易行,是水质评价的一种有效方法,具有广阔的应用前景.     

Quadratic Störmer‐type methods for the solution of the Boussinesq equation by the method of lines

We study the numerical treatment of Boussinesq PDE equation using the method of lines. For the space discretization, we choose either classical finite differences or Fourier pseudospectral methods. Both cases result in a system of second‐order ordinary differential equations (ODEs) that is quadratic. In order to take advantage of this special feature, we choose to solve the ODE system using a new type of hybrid Numerov method specially constructed for such problems. Other efficient ODE solvers taken from the literature are used to solve the system of ODEs as well. By taking all the combinations of space discretization methods and ODE solvers, we discuss the stability and accuracy features revealed from the numerical tests. © 2008 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq 2008     

Optimizing feature selection to improve medical diagnosis

In this paper, we propose a new optimization framework for improving feature selection in medical data classification. We call this framework Support Feature Machine (SFM). The use of SFM in feature selection is to find the optimal group of features that show strong separability between two classes. The separability is measured in terms of inter-class and intra-class distances. The objective of SFM optimization model is to maximize the correctly classified data samples in the training set, whose intra-class distances are smaller than inter-class distances. This concept can be incorporated with the modified nearest neighbor rule for unbalanced data. In addition, a variation of SFM that provides the feature weights (prioritization) is also presented. The proposed SFM framework and its extensions were tested on 5 real medical datasets that are related to the diagnosis of epilepsy, breast cancer, heart disease, diabetes, and liver disorders. The classification performance of SFM is compared with those of support vector machine (SVM) classification and Logical Data Analysis (LAD), which is also an optimization-based feature selection technique. SFM gives very good classification results, yet uses far fewer features to make the decision than SVM and LAD. This result provides a very significant implication in diagnostic practice. The outcome of this study suggests that the SFM framework can be used as a quick decision-making tool in real clinical settings.     

利用人的手部数据推断身高的研究

利用男性(52例)成人人体手部的有关数据(手长、手宽、掌长、掌宽、拇指长、食指长、中指长、环指长、小指长)与身高数据应用多元线性回归统计数值计算软件系统,得出上述9个指标值推断身高的回归方法,经过结果分析,有其应用价值。