基于SVM理论的一种新的数据分类方法

基于 SVM分类器在模式识别问题中有独特的优势 ,本文通过对标准 SVM模型的改造 ,提出了一种新的简单的数据分类方法 .理论分析和实验表明 ,该方法与标准 SVM分类方法相比具有处理大规模数据识别的能力且保持较高的样本识别率 ,节省存储空间等优势 .     

基于SVM的高分辨率遥感图像的分类方法研究

SVM(Support Vector Machine,支持向量机)分类算法是一种在高分辨率遥感图像分类中逐步得到重视的算法.首先详细介绍了SVM算法的数学原理,其次给出了基于SVM的高分辨率遥感图像分类方法的模型建立、光谱特征提取以及分类器设计,然后在AVIRIS标准多波段遥感数据集Indian Pines上进行了仿真,通过混淆矩阵和kappa系数评价了分类性能,最后以作者所在高校地区高分图像分类为例,验证了方法在高分辨率遥感图像地物分类上的有效性.     

Grid-Search和PSO优化的SVM在Shibor回归预测中的应用研究

作为一种动态和非稳定时间序列,Shibor发展变化是随机波动的,难以准确预测Shibor的波动性.支持向量机(SVM)在回归预测非线性时间序列方面有很好地预测效果,SVM的预测精度和泛化能力的核心是参数的优化选择,分别用网格搜索法(Grid-Search)和粒子群(PSO)算法来优化SVM的参数c和g.从而将参数优化后的SVM非线性回归预测法与基于传统ARIMA时间序列预测结果进行对比分析.实验表明,优化后的SVM回归预测方法比ARIMA时间序列方法更精确,在实际中具有很大的应用价值.     

基于非对称半监督集成SVM的托攻击检测方法

受推荐系统在电子商务领域重大经济利益的驱动,恶意用户以非法牟利为目的实施托攻击,操纵改变推荐结果,使推荐系统面临严峻的信息安全威胁,如何识别和检测托攻击成为保障推荐系统信息安全的关键。传统支持向量机(SVM)方法同时受到小样本和数据不均衡两个问题的制约。为此,提出一种半监督SVM和非对称集成策略相结合的托攻击检测方法。首先训练初始SVM,然后引入K最近邻法优化分类面附近样本的标记质量,利用标记数据和未标记数据的混合样本集减少对标记数据的需求。最后,设计一种非对称加权集成策略,重点关注攻击样本的分类准确率,降低集成分类器对数据不均衡的敏感性。实验结果表明,本文方法有效地解决了小样本问题和数据不均衡分布问题,获得了较好的检测效果。     

基于相异度的SVM选择性集成雾霾天气预测方法

目前雾霾污染日益严重,威胁到了环境保护和人类健康,需要对雾霾天气进行预测.通过对多个支持向量机(SVM)进行选择性集成,克服单个SVM不稳定的缺点,提出了基于相异度的SVM选择性集成雾霾天气预测方法(DSE-SVM).首先采用高斯核SVM独立训练出多个个体SVM;其次计算出个体SVM的相异度,剔除相异度最大的个体SVM;最后运用多数投票算法对剩余的SVM进行集成,并进行了理论分析.通过对北京、上海和广州三地区近两年的雾霾数据进行实验分析,实验结果表明DSE-SVM方法预测性能更优,具有较高的稳定性和可信性.     

基于非凸惩罚的SVM模型对科技型中小企业信用风险评估

以科技型中小企业为研究对象,从企业的盈利能力、成长能力、运营能力、偿债能力、供应链因素五方面选取了17个影响因素,运用带有非凸惩罚的SVM模型(SCAD SVM)模型对影响中小企业的信用风险因素进行研究,并选用LassoSVM和SVM作为对比,进行变量选择和参数估计,最后对模型的准确率进行预测,得出结论:Lasso SVM方法倾向于留下一些不太重要的变量,而SCAD SVM方法通过将系数大的变量保留,系数小的直接减小为0的方式,可以选择出重要的变量,通过预测精度验证发现,SCAD SVM方法比Lasso SVM和SVM的预测精度更高.     

基于支持向量机的大豆产量预测研究

应用支持向量机(SVM)的算法进行中国大豆产量的预测研究,用1991-2008年中国大豆数据组成样本集,建立影响因素与大豆产量之间的SVM模型.利用SVM对输入和输出数据进行训练学习,逼近历史数据所隐含的函数关系,完成对新数据序列的映射关系,从而完成对未来年份大豆的预测,并与其它几种方法的预测效果进行比较.结果表明,SVM预测模型预测大豆产量的精度优于其它预测方法.     

基于SVM-FCE的网络学习评价系统

简单介绍SVM的基本理论的基础上,利用SVM和模糊综合评价方法的理论,建立评价指标体系及模糊综合评价模型,解决了网络教学过程中学生学习效果的评价问题,实验表明,运用SVM和模糊综合评价相结合的方法评价网络学习比其他评价方法更加客观、有效.     

基于加权支持向量机的VaR计算方法研究

针对统计学框架下传统VaR计算方法的不足，发展了基于加权支持向量机（W—SVM）的VaR计算新方法．为了在VaR模型中计入金融时间序列的记忆效应，采用最优市场因子作为支持向量机的加权模型．对2001—2009年上证综指的实证研究表明，基于W—SVM的VaR模型优于传统的VaR方法，在小样本、厚尾、非线性及有异常波动的市场条件下，各种置信度下的W—SVM方法均能取得较好的性能．     

基于CCH的SVM几何算法及其应用

支持向量机(support vector machine(SVM))是一种数据挖掘中新型机器学习方法.提出了基于压缩凸包(compressed convex hull(CCH))的SVM分类问题的几何算法.对比简约凸包(reducedconvex hull(RCH)),CCH保持了数据的几何体形状,并且易于得到确定其极点的充要条件.作为CCH的实际应用,讨论了该几何算法的稀疏化方法及概率加速算法.数值试验结果表明所讨论的算法可降低核计算并取得较好的性能.     

Regularization Networks and Support Vector Machines

Regularization Networks and Support Vector Machines are techniques for solving certain problems of learning from examples – in particular, the regression problem of approximating a multivariate function from sparse data. Radial Basis Functions, for example, are a special case of both regularization and Support Vector Machines. We review both formulations in the context of Vapnik's theory of statistical learning which provides a general foundation for the learning problem, combining functional analysis and statistics. The emphasis is on regression: classification is treated as a special case. This revised version was published online in June 2006 with corrections to the Cover Date.     

鲁棒半监督ν-支持向量分类机

支持向量机在近十年成为机器学习的主要学习技术,而且已经成功应用到有监督学习问题中。Fung和Mangasarian利用支持向量机对于既有已标类别样本又有未知类别样本的训练集进行训练,方法主要是利用少量已标明类别的样本进行训练得到一个分类器的同时对于未标明类别的样本进行分类,使得间隔最大化。此优化问题中假定样本是精确的,而在现实生活中,样本通常带有统计误差。因此,考虑样本带有扰动信息的半监督两类分类问题,给出鲁棒半监督v-支持向量分类算法。该算法的参数v易于选择,而数值试验也表明该算法具有良好的稳定性和较好的分类结果。     

基于支持向量机的拟南芥基因表达数据分析

针对拟南芥根部基因表达数据分析的问题,本文提出了一种新的基于距离度量学习的支持向机多分类算法．鉴于此问题的特殊性,本文通过最小化4分类机的LOO 误差来求得一个恰当的距离度量．并在此度量下找到若干个属于第5类(其它类)的训练点,从而构造出一个5分类机用来对所有基因分类．实验验证了此算法的可行性,并且比基因表达分析中传统使用的聚类方法更有效．     

Interpretable support vector machines for functional data

Support Vector Machines (SVMs) is known to be a powerful nonparametric classification technique even for high-dimensional data. Although predictive ability is important, obtaining an easy-to-interpret classifier is also crucial in many applications. Linear SVM provides a classifier based on a linear score. In the case of functional data, the coefficient function that defines such linear score usually has many irregular oscillations, making it difficult to interpret.     

SVMs在微阵列表达数据分析中的应用

微阵列技术允许同时录制成百万的基因表达水平。但由于经费和工艺的限制,目前研究者获得的表达数据集往往包含少量的样本,而基因表达的测量值却有上万条。很多传统的统计方法无法分析这样的数据,本文结合数据挖掘中统计学习理论的相关知识,详细介绍了一种有监督分析方法———支持向量机(SVMs)在微阵列表达数据分析中的应用。     

Parallel algorithm for training multiclass proximal Support Vector Machines

In this paper we describe a proximal Support Vector Machine algorithm for multiclassification problem by one-vs-all scheme. The computational requirement for the new algorithm is almost the same as training one of its element binary proximal Support Vector Machines. Low rank approximation is taken to reduce computational costs when the kernel matrix is too large. An error bound estimation for the approximated solution is given, which is used as a stopping criteria for low rank approximation. A post-processing strategy is developed to overcome the difficulty arising from unbalanced data and to improve the classification accuracy. A parallel implementation of the algorithm using standard MPI communication routines is provided to handle large-scale problems and to accelerate the training process. Experiment results on several public datasets validate the effectiveness of our proposed algorithm.     

Asset portfolio optimization using support vector machines and real-coded genetic algorithm

This paper presents an integrated approach for portfolio selection in a multicriteria decision making framework. Firstly, we use Support Vector Machines for classifying financial assets in three pre-defined classes, based on their performance on some key financial criteria. Next, we employ Real-Coded Genetic Algorithm to solve a mathematical model of the multicriteria portfolio selection problem in the respective classes incorporating investor-preferences.     

Heuristic approaches for support vector machines with the ramp loss

Recently, Support Vector Machines with the ramp loss (RLM) have attracted attention from the computational point of view. In this technical note, we propose two heuristics, the first one based on solving the continuous relaxation of a Mixed Integer Nonlinear formulation of the RLM and the second one based on the training of an SVM classifier on a reduced dataset identified by an integer linear problem. Our computational results illustrate the ability of our heuristics to handle datasets of much larger size than those previously addressed in the literature.     

Pattern recognition at different scales: A statistical perspective

In this paper we borrow concepts from Information Theory and Statistical Mechanics to perform a pattern recognition procedure on a set of X-ray hazelnut images. We identify two relevant statistical scales, whose ratio affects the performance of a machine learning algorithm based on statistical observables, and discuss the dependence of such scales on the image resolution. Finally, by averaging the performance of a Support Vector Machines algorithm over a set of training samples, we numerically verify the predicted onset of an “optimal” scale of resolution, at which the pattern recognition is favoured.     

Pattern classification by goal programming and support vector machines

Support Vector Machines (SVMs) are now very popular as a powerful method in pattern classification problems. One of main features of SVMs is to produce a separating hyperplane which maximizes the margin in feature space induced by nonlinear mapping using kernel function. As a result, SVMs can treat not only linear separation but also nonlinear separation. While the soft margin method of SVMs considers only the distance between separating hyperplane and misclassified data, we propose in this paper multi-objective programming formulation considering surplus variables. A similar formulation was extensively researched in linear discriminant analysis mostly in 1980s by using Goal Programming(GP). This paper compares these conventional methods such as SVMs and GP with our proposed formulation through several examples.Received: September 2003, Revised: December 2003,