Support vector machines for face recognition

Support vector machines (SVMs) have been recently proposed as a new learning network for bipartite pattern recognition. In this paper, SVMs incorporated with a binary tree recognition strategy are proposed to tackle the multi-class face recognition problem. The binary tree extends naturally, the pairwise discrimination capability of the SVMs to the multi-class scenario. Two face databases are used to evaluate the proposed method. The performance of the SVMs based face recognition is compared with the standard eigenface approach, and also the more recently proposed algorithm called the nearest feature line (NFL).     

Fuzzy rules extraction from support vector machines for multi-class classification

This paper proposes a new method for fuzzy rule extraction from trained support vector machines (SVMs) for multi-class problems, named FREx_SVM. SVMs have been used in a variety of applications. However, they are considered “black box models,” where no interpretation about the input–output mapping is provided. Some methods to reduce this limitation have already been proposed, but they are restricted to binary classification problems and to the extraction of symbolic rules with intervals or functions in their antecedents. In order to improve the interpretability of the generated rules, this paper presents a new model for extracting fuzzy rules from a trained SVM. The proposed model is suited for classification in multi-class problems and includes a wrapper feature selection algorithm. It is evaluated in four benchmark databases, and results obtained demonstrate its capacity to generate a reduced set of interpretable fuzzy rules that explains both the classification database and the influence of each input variable on the determination of the final class.     

基于SVM 的二叉树多类分类算法及其在故障诊断中的应用

基于结构风险最小化原则的支持向量机(SVM)对小样本决策具有较好的学习推广性。但由于常规SVM算法是从2类分类问题推导出的，在解决故障诊断这种典型的多类分类问题时存在因雄，因而提出一种依赖故障优先级的基于SVM的二叉树多级分类器实现(2PTMC)方法，该方法具有简单、直观，重复训练样本少的优点。通过将其应用于柴油机振动信号的故障诊断，获得了令人满意的效果。     

Process knowledge based multi-class support vector classification (PK-MSVM) approach for surface defects in hot rolling

Random surface defects occur during the hot bar rolling of steels and are identified either by manual or by automated inspection techniques. Manual inspection techniques are purely based on the process knowledge of the inspector such as the location, type and kind of defects, and the primary sources of these defects. The automated techniques, to identify and classify the defects, rely on machine vision technologies and image processing algorithms based on support vector machines, wavelets, image processing and statistical inference. Both these approaches have their own advantages and limitations. To improve the accuracy of classification of these defects a process knowledge based support vector classification scheme is proposed (called PK-MSVM) which combines feature extraction task of automated inspection with the process knowledge. The defect observation data from the imaging sensor is transformed to include this process knowledge. Three attributes of the defects – length to width ratio, longitudinal location and transverse location- are used for this transformation are they are closely related to the thermo-mechanics of the rolling process. Different formulations of the multi-class support vector machines (MSVMs) are compared for this classification with or without process knowledge based transformation: one-against-one, one-against-all and Hastie’s algorithm of multi class SVM. It is found that the new approach (PK-MSVM) performs better than traditional MSVM for all the three formulations. For the best case, the performance sees a jump of more than 100%. Thus incorporating process knowledge in identification and classification does increase the reliability of inspection considerably.     

改进的超球支持向量机算法

超球支持向量机算法用于解决多类别数据的分类问题。对超球重叠区域的数据正确分类对球结构支持向量机的分类性能至关重要。在分析这些样本点特点的基础上,提出了一种新的分类规则,使超球支持向量机算法的泛化性能高于现有的算法。实验结果表明该算法有效可行,提高了最小包围球分类器的分类精度。     

A novel pattern recognition algorithm: Combining ART network with SVM to reconstruct a multi-class classifier

Based on the principle of one-against-one support vector machines (SVMs) multi-class classification algorithm, this paper proposes an extended SVMs method which couples adaptive resonance theory (ART) network to reconstruct a multi-class classifier. Different coupling strategies to reconstruct a multi-class classifier from binary SVM classifiers are compared with application to fault diagnosis of transmission line. Majority voting, a mixture matrix and self-organizing map (SOM) network are compared in reconstructing the global classification decision. In order to evaluate the method’s efficiency, one-against-all, decision directed acyclic graph (DDAG) and decision-tree (DT) algorithm based SVM are compared too. The comparison is done with simulations and the best method is validated with experimental data.     

基于多类支持向量机的棉花异性纤维分类方法

提出一种基于多类支持向量机的棉花异性纤维分类方法,以期解决棉花异性纤维的在线分类难题。该方法首先对异性纤维目标图像进行颜色、形状和纹理特征提取,形成用于精确描述异性纤维目标的特征向量;然后分别构建3种不同体系结构的多类支持向量机用于棉花异性纤维的分类;最后采用交叉验证法对所构建的3种多类支持向量机进行测试。测试结果表明,基于有向无环图的一对一多类支持向量机在分类精度和分类速度上更适合用于棉花异性纤维在线分类。     

一种基于混合二叉树结构的多类支持向量机分类算法

为提高多类支持向量机的分类效率,提出了一种基于混合二叉树结构的多类支持向量机分类算法。该混合二叉树中的每个内部结点对应一个分割超平面,该超平面通过计算两个距离最远的类的质心而获得,即该超平面为连接两质心线段的垂直平分线。每个终端结点(即决策结点)对应一个支持向量机,它的训练集不再是质心而是两类(组)样本集。该分类模型通常是超平面和支持向量机的混合结构,其中超平面实现训练早期的近似划分,以提升分类速度;而支持向量机完成最终的精确分类,以保证分类精度。实验结果表明,相比于经典的多类支持向量机方法,该算法在保证分类精度的前提下,能够有效缩短计算时间,提升分类效率。     

支持向量机的缺陷及改进算法

传统支持向量机通常关注于数据分布的边缘样本,支持向量通常在这些边缘样本中产生。本文提出一个新的支持向量算法,该算法的支持向量从全局的数据分布中产生,其稀疏性能在大部分数据集上远远优于经典支持向量机算法。该算法在多类问题上的时间复杂度仅等价于原支持向量机算法的二值问题,解决了设计多类算法时变量数目庞大或者二值子分类器数目过多的问题。     

Fingerprint classification using one-vs-all support vector machines dynamically ordered with naı¨ve Bayes classifiers

Fingerprint classification reduces the number of possible matches in automated fingerprint identification systems by categorizing fingerprints into predefined classes. Support vector machines (SVMs) are widely used in pattern classification and have produced high accuracy when performing fingerprint classification. In order to effectively apply SVMs to multi-class fingerprint classification systems, we propose a novel method in which the SVMs are generated with the one-vs-all (OVA) scheme and dynamically ordered with na?¨ve Bayes classifiers. This is necessary to break the ties that frequently occur when working with multi-class classification systems that use OVA SVMs. More specifically, it uses representative fingerprint features as the FingerCode, singularities and pseudo ridges to train the OVA SVMs and na?¨ve Bayes classifiers. The proposed method has been validated on the NIST-4 database and produced a classification accuracy of 90.8% for five-class classification with the statistical significance. The results show the benefits of integrating different fingerprint features as well as the usefulness of the proposed method in multi-class fingerprint classification.     

一种改进的DDAGSVM多类分类方法

支持向量机最初是针对两类分类问题提出的,如何有效地将其推广到多类分类问题仍是一项有待研究的课题。本文介绍了现有的具有代表性的多类支持向量机分类算法,并在分析决策导向非循环图支持向量机分类器生成顺序随机化的基础上,引入类内的分散度,以基于样本分布的类间分离程度作为类别的划分顺序,最终构成了一种分类间隔较大的决策导向非循环图支持向量机分类算法。实验结果表明了本文方法具有更高的分类精度。     

A new maximal-margin spherical-structured multi-class support vector machine

Support vector machines (SVMs), initially proposed for two-class classification problems, have been very successful in pattern recognition problems. For multi-class classification problems, the standard hyperplane-based SVMs are made by constructing and combining several maximal-margin hyperplanes, and each class of data is confined into a certain area constructed by those hyperplanes. Instead of using hyperplanes, hyperspheres that tightly enclosed the data of each class can be used. Since the class-specific hyperspheres are constructed for each class separately, the spherical-structured SVMs can be used to deal with the multi-class classification problem easily. In addition, the center and radius of the class-specific hypersphere characterize the distribution of examples from that class, and may be useful for dealing with imbalance problems. In this paper, we incorporate the concept of maximal margin into the spherical-structured SVMs. Besides, the proposed approach has the advantage of using a new parameter on controlling the number of support vectors. Experimental results show that the proposed method performs well on both artificial and benchmark datasets.     

一种LDA与SVM混合的多类分类方法

针对决策有向无环图支持向量机（ＤＤＡＧＳＶＭ）需训练大量支持向量机（ＳＶＭ）和误差积累的问题,提出一种线性判别分析（ＬＤＡ）与ＳＶＭ 混合的多类分类算法．首先根据高维样本在低维空间中投影的特点,给出一种优化ＬＤＡ 分类阈值;然后以优化ＬＤＡ 对每个二类问题的分类误差作为类间线性可分度,对线性可分度较低的问题采用非线性ＳＶＭ 加以解决,并以分类误差作为对应二类问题的可分度;最后将可分度作为混合ＤＤＡＧ 分类器的决策依据．实验表明,与ＤＤＡＧＳＶＭ 相比,所提出算法在确保泛化精度的条件下具有更高的训练和分类速度．     

Integrating nonlinear graph based dimensionality reduction schemes with SVMs for credit rating forecasting

By integrating graph based nonlinear dimensionality reduction with support vector machines (SVMs), this study develops a novel prediction model for credit ratings forecasting. SVMs have been successfully applied in numerous areas, and have demonstrated excellent performance. However, due to the high dimensionality and nonlinear distribution of the input data, this study employed a kernel graph embedding (KGE) scheme to reduce the dimensionality of input data, and enhance the performance of SVM classifiers. Empirical results indicated that one-vs-one SVM with KGE outperforms other multi-class SVMs and traditional classifiers. Compared with other dimensionality reduction methods the performance improvement owing to KGE is significant.     

非平衡二叉树多类支持向量机分类方法

提出一种新的基于非平衡二叉树的支持向量机多类别分类方法。该方法通过分析已知类别样本的先验分布知识,构造一个二叉决策树,使容易区分的类别从根节点开始逐层分割出来,以获得较高的推广能力。该方法解决了传统分类算法中所存在的不可分区域问题,在训练时只需构造N-1个SVM分类器,而测试时的判决次数小于N。将该方法应用于人脸识别实验。测试结果表明,与传统分类算法相比,该方法的平均分类时间是最少的。     

基于决策树SVM分类器的感兴趣区域定位方法

感兴趣区域定位是提取目标特征,进行目标识别与跟踪等后续处理的重要基础.由于大尺寸遥感图像的光谱特性和目标形状均很复杂,通常采用的基于光谱特征的分割方法和基于边缘的区域生长技术不合适,从模式分类角度考虑遥感图像中感兴趣区域快速定位问题,提出一种基于决策二叉树支持向量机的纹理分类方法,将分类器分布在各个结点上,构成了多类支持向量机,减少了分类器数量和重复训练样本的数量.在SPOT图像上的实验结果表明,该方法实现感兴趣区域的快速定位有较高的分类正确率.     

二叉树多类SVM在网络入侵检测中的应用

本文在考察现有多类分类支持向量机(SVM)算法后,提出了一种基于二叉树结构的多分类器融合思想,融合过程充分考虑了类别之间的区分度,从而建立一颗相对优化的二叉树SVM的多类分类算法,并把改进后的多类SVM应用于入侵检测中以提高系统性能。在KDDCUP1999数据集上的实验结果表明了本方法的有效性。     

Evolutionary wrapper approaches for training set selection as preprocessing mechanism for support vector machines: Experimental evaluation and support vector analysis

One of the most powerful, popular and accurate classification techniques is support vector machines (SVMs). In this work, we want to evaluate whether the accuracy of SVMs can be further improved using training set selection (TSS), where only a subset of training instances is used to build the SVM model. By contrast to existing approaches, we focus on wrapper TSS techniques, where candidate subsets of training instances are evaluated using the SVM training accuracy. We consider five wrapper TSS strategies and show that those based on evolutionary approaches can significantly improve the accuracy of SVMs.     

一种新的分裂层次聚类SVM多值分类器

提出一种分裂层次聚类SVM分类树分类方法.该方法通过融合模糊聚类技术和支持向量机算法,利用分裂的层次聚类策略,有选择地重新构造学习样本集和SVM子分类器,得到了一种树形多值分类器.研究结果表明,对于k类别模式识别问题,该方法只需构造k-1个SVM子分类器,克服了SVM子分类器过多以及存在不可区分区域的缺点,具有良好的分类性能.实验结果验证了该方法的优越性.