基于Huffman树的多类SVM方法

提出了一种基于Huffman树的支持向量机多类分类方法.二叉树方法是一种常用的多类分类方法,它的关键问题在于如何构造合理的结构以获得较高的推广能力.为解决该问题,按照Huffman树的构造过程自下向上地构造二又树,使易于分割的类处于上层结点.实验结果表明,该方法与One-vs-One和DAGSVM方法的分类效果相当.     

基于支持向量机的货币识别应用研究

支持向量机是一种新的基于统计学习理论的机器学习算法,它可以应用于小样本、非线性和高维模式识别。研究了支持向量机的学习算法,依据支持向量机的特点采用了相应的货币特征数据获取及预处理方法,提出采用改进SMO训练算法和DAGSVM多值分类算法构建的支持向量机用于货币识别,从而达到对货币高效、准确识别。实验结果证实了该方案的有效性。     

基于决策二叉树的多类支持向量机在大气质量评定中的应用

经典的支持向量机(Support Vector Machine，简称SVM)是针对二类分类的，在解决大气质量等级这种典型的多类分类问题时存在困难，本文提出了基于决策二叉树支持向量机的大气质量分类算法，将分类器分布在各个结点上，从而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。实验结果表明该方法能够正确地对大气质量进行识别。     

一种设计层次支持向量机多类分类器的新方法

层次结构的设计是层次支持向量机多类分类方法应用中的关键问题,类间可分性是设计层次结构的重要依据,提出了一种基于线性支持向量机度量类间相似程度的方法,并给出了一种基于类间可分性设计层次支持向量机多类分类器的新方法。实验表明,新方法有效地提高了层次支持向量机多类分类器的分类精度和速度。     

基于支持向量机的文本兼类标注

该文分析了现有多类别支持向量机分类器的特点及DAGSVM的优势,并结合模糊技术改造DAGSVM使之能进行兼类标注的多类别分类。改进后的FDAGSVM采用模糊决策面代替了DAGSVM的分明决策面,使判决过程适应兼类标注的要求,克服了传统的多类别分类支持向量机必然将样本分入某一类别的不足。基准数据的兼类标注多类别分类试验表明,FDAGSVM在文本的兼类标注分类中表现出较好的性能。     

基于超球体的二叉树SVM多分类算法的研究

针对现有的支持向量机在多类分类方法上存在的不足,提出了一种基于超球体的二叉树SVM多类分类算法。该算法利用球结构的SVM考虑了每个类的分布情况,能有效地处理不平衡样本数据,设计超球体支持向量机的树型模型,克服了差错积累问题。实验证明,与其它SVM多类分类方法相比,该方法具有较高的分类精度,提高了支持向量机在多类分类问题中的实验效果。     

去边缘模糊支持向量机

针对两类分类问题中样本点数量多,类别模糊且有孤立野点的情况,提出了去边缘模糊支持向量机。该方法用一类分类思想,预先去掉那些可能不是支持向量的点,并引入了模糊隶属度计算公式,使其适合模糊分类的性能特点。从理论和实证分析两个方面将该方法与一般的模糊支持向量机进行了对比分析,结果显示该方法不但大大减少了训练点数目,从而减少了内存和计算量,提高了训练速度和分类准确率。     

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。     

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。     

基于最小二乘支持向量机的非平衡分布数据分类

支持向量机是在统计学习理论基础上发展起来的一种十分有效的分类方法。然而当两类样本数量相差悬殊时,会引起支持向量机分类能力的下降。为了提高支持向量机的非平衡数据分类能力,文章分析了最小二乘支持向量机的本质特征,提出了一种非平衡数据分类算法。在UCI标准数据集上进行的实验表明,该算法能够有效提高支持向量机对非均衡分布数据的正确性,尤其对于大规模训练集的情况,该算法在保证不损失训练精度的前提下,使训练速度有较大提高。     

A new fast algorithm for multiclass hyperspectral image classification with SVM

The support vector machine (SVM) has been a dominant machine-learning technique in the last decade and has demonstrated its efficiency in many applications. Research on classification of hyperspectral images have shown the efficiency of this method to overcome the Hughes phenomenon for classification of such images. A major drawback of classification by SVM is that this classifier was originally developed to solve binary problems, and the algorithms for multiclass problems usually have a high-computational load. In this article, a new and fast method for multiclass problems is proposed. This method has two stages. In the first stage, samples are classified by a maximum likelihood (ML) classifier, and in the second stage, SVM selects the final label of a sample among high-probability classes for that sample by a tree structure. So, for each sample, only some classes must be searched by SVM to find its label. The uncertainty of ML classification for a sample is obtained by the entropy of probabilities, and the number of classes that must be searched by SVM for a sample is obtained based on the uncertainty of that sample in the primary ML classification. This approach is compared with two widely used multiclass algorithms: one-against-one (OAO) and directed acyclic graph (DAGSVM). The obtained results on real data from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) revealed less computational time and better accuracy compared to these multiclass algorithms.     

A decision based one-against-one method for multi-class support vector machine

The support vector machine (SVM) has a high generalisation ability to solve binary classification problems, but its extension to multi-class problems is still an ongoing research issue. Among the existing multi-class SVM methods, the one-against-one method is one of the most suitable methods for practical use. This paper presents a new multi-class SVM method that can reduce the number of hyperplanes of the one-against-one method and thus it returns fewer support vectors. The proposed algorithm works as follows. While producing the boundary of a class, no more hyperplanes are constructed if the discriminating hyperplanes of neighbouring classes happen to separate the rest of the classes. We present a large number of experiments that show that the training time of the proposed method is the least among the existing multi-class SVM methods. The experimental results also show that the testing time of the proposed method is less than that of the one-against-one method because of the reduction of hyperplanes and support vectors. The proposed method can resolve unclassifiable regions and alleviate the over-fitting problem in a much better way than the one-against-one method by reducing the number of hyperplanes. We also present a direct acyclic graph SVM (DAGSVM) based testing methodology that improves the testing time of the DAGSVM method.     

A comparison of methods for multiclass support vector machines

Support vector machines (SVMs) were originally designed for binary classification. How to effectively extend it for multiclass classification is still an ongoing research issue. Several methods have been proposed where typically we construct a multiclass classifier by combining several binary classifiers. Some authors also proposed methods that consider all classes at once. As it is computationally more expensive to solve multiclass problems, comparisons of these methods using large-scale problems have not been seriously conducted. Especially for methods solving multiclass SVM in one step, a much larger optimization problem is required so up to now experiments are limited to small data sets. In this paper we give decomposition implementations for two such "all-together" methods. We then compare their performance with three methods based on binary classifications: "one-against-all," "one-against-one," and directed acyclic graph SVM (DAGSVM). Our experiments indicate that the "one-against-one" and DAG methods are more suitable for practical use than the other methods. Results also show that for large problems methods by considering all data at once in general need fewer support vectors.     

一种改进的DAGSVM 手势识别方法及其应用

在支持向量机多分类方法基础上,提出了一种改进的有向无环图支持向量机( Directed Acyclic Graph Support Vector Machine,DAGSVM) 手势识别方法。首先根据Kinect 采集到 的场景深度信息将前景和背景分开,分割得到手,然后提取其特征向量,利用特征向量训练多 个SVM 两分类器,采用DAG 拓扑结构构成DAGSVM 多分类器,并对其结构排序进行改进。 实验证明,与其他支持向量机多分类方法相比,改进后的DAGSVM 分类器能够达到更高的识 别率,并将这个手势识别方法用于智能轮椅的控制上,收到了良好的效果。     

Electrocardiogram beat classification using empirical mode decomposition and multiclass directed acyclic graph support vector machine

In this paper, a classifier motivated from statistical learning theory, i.e., support vector machine, with a new approach based on multiclass directed acyclic graph has been proposed for classification of four types of electrocardiogram signals. The motivation for selecting Directed Acyclic Graph Support Vector Machine (DAGSVM) is to have more accurate classifier with less computational cost. Empirical mode decomposition and subsequently singular value decomposition have been used for computing the feature vector matrix. Further, fivefold cross-validation and particle swarm optimization have been used for optimal selection of SVM model parameters to improve the performance of DAGSVM. A comparison has been made between proposed algorithm and other two classifiers, i.e., K-Nearest Neighbor (KNN) and Artificial Neural Network (ANN). The DAGSVM has yielded an average accuracy of 98.96% against 95.83% and 96.66% for the KNN and the ANN, respectively. The results obtained clearly confirm the superiority of the DAGSVM approach over other classifiers.     

Adaptive binary tree for fast SVM multiclass classification

This paper presents an adaptive binary tree (ABT) to reduce the test computational complexity of multiclass support vector machine (SVM). It achieves a fast classification by: (1) reducing the number of binary SVMs for one classification by using separating planes of some binary SVMs to discriminate other binary problems; (2) selecting the binary SVMs with the fewest average number of support vectors (SVs). The average number of SVs is proposed to denote the computational complexity to exclude one class. Compared with five well-known methods, experiments on many benchmark data sets demonstrate our method can speed up the test phase while remain the high accuracy of SVMs.     

基于混合粒子群算法的运动估计研究

针对块匹配运动估计算法中传统搜索方法的不足,提出了一种新的基于混合粒子群的块匹配运动估计算法。在保留系统随机搜索性能的同时根据运动矢量特性合理地设计初始搜索种群,并通过混沌差分进化搜索协同粒子群算法迭代寻优,混沌序列用于优化差分变异算子,以提高算法的精细搜索能力。通过相同点检测技术和恰当的终止计划有效地降低了系统的运算复杂度。经实验测试与验证,该算法在搜索质量和运算复杂度中达到了一种动态平衡的状态,其整体性能高于传统的快速运动估计算法,效果更逼近于穷举搜索法。     

Multiclass Lagrangian support vector machine

A support vector machine (SVM) has been developed for two-class problems, although its application to multiclass problems is not straightforward. This paper proposes a new Lagrangian SVM (LSVM) for application to multiclass problems. The multiclass Lagrangian SVM is formulated as a single optimization problem considering all the classes together, and a training method tailored to the multiclass problem is presented. A multiclass output representation matrix is defined to simplify the optimization formulation and associated training method. The proposed method is applied to some benchmark datasets in repository, and its effectiveness is demonstrated via simulation.     

后验概率在多分类支持向量机上的应用

支持向量机是基于统计学习理论的一种新的分类规则挖掘方法。在已有多分类支持向量机基础上,首次提出了几何距离多分类支持向量分类器;随后,将二值支持向量机的后验概率输出也推广到多分类问题,避免了使用迭代算法,在快速预测的前提下提高了预测准确率。数值实验的结果表明,这两种方法都具有很好的推广性能,能明显提高分类器对未知样本的分类准确率。     

Binary tree of SVM: a new fast multiclass training and classification algorithm

We present a new architecture named Binary Tree of support vector machine (SVM), or BTS, in order to achieve high classification efficiency for multiclass problems. BTS and its enhanced version, c-BTS, decrease the number of binary classifiers to the greatest extent without increasing the complexity of the original problem. In the training phase, BTS has N-1 binary classifiers in the best situation (N is the number of classes), while it has log/sub 4/3/((N+3)/4) binary tests on average when making a decision. At the same time the upper bound of convergence complexity is determined. The experiments in this paper indicate that maintaining comparable accuracy, BTS is much faster to be trained than other methods. Especially in classification, due to its Log complexity, it is much faster than directed acyclic graph SVM (DAGSVM) and ECOC in problems that have big class number.