Research on Kernel Selection of Support Vector Machine

由Vapnik等人提出的支持向量机（SupportVectorMachine，SVM）技术，由于具有极强的模型泛化能力，不会陷入局部极小点，以及很强的非线性处理能力等特点，近十年来取得了全面飞速的发展，获得了大量成功的应用，已成为模式识别中最为活跃的研究领域之一。 当前，选择合适的核函数及其参数（核选择）已成为SVM进一步发展的关键点和难点。核函数决定了SVM的非线性处理能力，也决定着分类函数的构造，而对具体问题而言，选择合适的核函数及其参数，还存在着许多的实际困难。 针对SVM中的核选择问题，本文对SVM的模型问题、特征空间线性可分的结构问题、核学习中基核的选择问题、以及核函数及其参数的...In the last ten years there have been very significant developments in the theoretical understanding of Support Vector Machines (SVMs) , proposed by Vapnik and others, as well as algorithmic strategies for implementing them, and applications of the approach to practical problems. Nowadays, the selection of the SVM-kernel with suitable form and parameters (Kernel Selection) has become a key-point...学位：工学博士院系专业：信息科学与技术学院自动化系_控制理论与控制工程学号：B20043100

A Novel Strategy for Improving the Performance of SVM Classification for Unbalance Distribution Data

支持向量机利用接近边界的少数向量来构造一个最优分类面。但是若两分类问题中的样本呈现非平衡分布时,即两类样本数目相差很大时,分类能力就会有所下降。提出分别使用重复数量少的一类样本、选择数量多的类样本以及引入类惩罚因子的三个方法来改善分类能力。实验表明,三种方法对不同类型数据集合,一定程度上都改善了支持向量的分类能力。Support vector machine constructs an optimal hyperplane utilizing a small set of vectors near boundary.However,when the two-class problem samples are imbalanced distribution,SVM has a poor performance.This article presents repeat training minority class samples,selects training majority class samples and introduces punish parameter three methods.Computational results indicate that it improves the capability of SVM classification for the unbalanced samples of different styles datasets

The research of multi-class problem based gene expression profile data

针对基因表达谱微阵列的数据多分类问题,给出一种在多病类情况下的基于信噪比和相关性的特征基因选择方法.该方法一次性考虑基因区分所有病类的能力,尽量避免基因的冗余性;其次利用支持向量机,构建了基因表达谱微阵列数据的多分类器;最后通过实验表明了本方法的有效性.Aiming at the multi-class problem of gene expression profile data,this paper proposes a gene selection method based on S2N and correlation for multiple diseases.This method takes the classification abilities of genes to separate all the diseases into consideration at a time and tries to avoid redundancy in selected genes.Secondly,we construct multi-classifier of gene expression profile data using SVM.Finally,we do experiment by this method,the result of which shows great effectiveness of the method.国家自然科学基金资助项目(60704042

传真管理信息系统的设计与实现

本文从实际应用角度出发,给出了传真管理信息系统的设计方案和实现方法,重点阐述了系统的设计与实现过程中如何有效地运用client/server技术、注册表技术、数据流技术和传真管理第三方组件技术,以提高系统的开发和运行效率。Based on the practical application,the desig n scheme and method of fax manag ement client/server system were posed.The author mainly discussed the client/server technolog y,reg edit technolog y,data stream technolog y and third party control of fax manag ement in this paper.The efficiency of development and func-tion was g reately improved

矢量量化在OCR特征库压缩中的应用

OCR(Optical Character Recognition)光学字符识别技术已被广泛应用于企业与个人的信息化处理,而随着嵌入式系统的发展,特别是中文手写识别技术的成熟,对系统容量与识别速度提出了新的要求.为了便于在资源有限的嵌入式硬件设备上实现OCR系统,寻求一种能保持识别率基本不变,又有较好压缩比的OCR特征库压缩方法是很有理论意义与商业应用价值的.本文通过对矢量量化算法作相应修改,用C++语言实现OCR特征库的压缩,并在实验中取得了良好的性能

Prediction Financial Distress of Firms Based on GA-SVM

摘要：通过遗传算法结合支持向量机算法中期望风险边界，对我国上市公司财务数据进行特征提取，并优化构造广义最优分类超平面，从而获得具有较好整体预测性能的联合模型。数值实验表明，该方法可以降低特征空间维数，具有较好的分类准确率。实证结果表明，GA-SVM联合预测模型具有可靠的预测财务困境能力，有着良好的应用前景。 Abstract：This paper uses genetic algorithm and support vector machine to set up a hybrid model of financial distress prediction in Chinese listed firms. Numerical simulation shows that the proposed method can reduce the dimension of the feature space, and has higher correct classification rate.As the result, the proposed GA-SVM hybrid model has reliable financial distress prediction ability, and it has a good application prospect in this area.厦门大学“985”计划基金资助项目“海量数据挖掘方法及应

A forecast of bulk-holding stock based on random forest

首先通过对基金重仓股的财务指标和市场指标的分析,建立一套科学合理的基金重仓股指标体系;其次利用随机森林建立基金重仓股的预测模型;最后通过实验验证了方法的有效性和优越性.本研究将为投资者提供一个投资决策的优良工具.Firstly we construct a system of scientific stock index by means of analysis of financing index and market index.Secondly we construct forecast model based on random forest.In the end,numerical results show that the method used is effective and advantageous.So this research provides an excellent decision-making tool for investors.国家自然科学基金资助项目(60704042

基于支持向量机的中国上市公司财务困境预测

针对传统预测模型的不足,探讨支持向量机(SupportVectorsMachine,SVM)模型在中国上市公司财务困境预测中的作用。通过SVM与传统的多元线性回归(Multi Linear Regression,MLR)和Logit分析(LogitAnalysis,LA)的实证对比和模型分析,得出SVM在20组测试样本集上的平均误判率是最好的,显著优于MLR,也优于LA,证实了SVM模型用于财务困境预测的有效性和优越性。教育部厦门大学211工程电子信息技术项目(0630-E11090

Empirical Analysis of Influencing Factors on Labor Wage in High Skill Worker Market by Apriori Algorithms

本文以广东为例,运用APrIOrI算法对广东高技能人才市场工资价位影响因素进行实证分析。研究结论认为影响高技能人才市场工资价位的主要因素与工种、地区、学历、技能、工龄、年龄有关,其中工种对收入的影响比其它都要大的多,特别是在分地区的分析中尤为明显。This paper uses Apriori algorithms to analyze the factors which influence the wage level of high skill workers,using the data from Guangdong.The main factors include job kind,location,education,skill,working years and ages.The most influence factor is the job kind under control of the location.广东省劳动和社会保障厅2008重点科研项目《高技能人才工资市场价位研究》(项目编号:2008A0803

Feature selection of random forest-based proximity matrix difference

将随机森林的相似度矩阵看做一种特殊的核度量,利用该度量对模型参数的鲁棒性和特征变化的敏感性,提出一种特征选择的方法.采用相似度矩阵,计算训练样本类内和类间相似性比率.再利用特征值随机置换技术,将相似性比率的变化量作为特征重要性度量指标,从而对所有特征进行排序.试验结果表明,该方法能充分利用全部样本的信息,有效地进行特征选择,且其性能优于基于袋外数据误差率估计的特征选择方法.A feature selection method is proposed,after analyzing proximity matrix′s to random forest model and its sensitiveness to the variation of features.Proximity matrix is taken as a special kernel measurement to compute the proximity ratio between inner-class and the inter-class,then permutes the values of feature randomly and the difference of proximity ratio was takes as the assessment criterion for feature importance.The process yields a ranking for all features.Experimental results show that the method achieves good effects and performs better than that of the method based on out-of-bag (OOB) error rate.福建省自然科学基金资助项目(2009J05153