共查询到18条相似文献,搜索用时 140 毫秒
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给出了一种基于编码二叉树的支持向量的多类分类算法。先定义了一种构造编码二叉树的方法,在此基础上合理的使用每个训练样本对应的编码来对多类样本进行划分,使之转化为两类分类问题。可以看出该算法可以大大减少子分类器的构造个数,从而简化了多类SVM分类算法。 相似文献
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给出了一种基于编码二叉树的支持向量的多类分类算法.先定义了一种构造编码二叉树的方法,在此基础上合理的使用每个训练样本对应的编码来对多类样本进行划分,使之转化为两类分类问题.可以看出该算法可以大大减少子分类器的构造个数,从而简化了多类SVM分类算法. 相似文献
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针对基于传统支持向量机(SVM)的多类分类算法在处理大规模数据时训练速度上存在的弱势,提出了一种基于对支持向量机(TWSVM)的多类分类算法。该算法结合二叉树SVM多类分类思想,通过在二叉树节点处构造基于TWSVM的分类器来达到分类目的。为减少二叉树SVM的误差累积,算法分类前首先通过聚类算法得到各类的聚类中心,通过比较各聚类中心之间的距离来衡量样本的差异以决定二叉树节点处类别的分离顺序,最后将算法用于网络入侵检测。实验结果表明,该算法不仅保持了较高的检测精度,在训练速度上还表现了一定优势,尤其在处理稍大规模数据时,这种优势更为明显,是传统二叉树SVM多类分类算法训练速度的近两倍,为入侵检测领域大规模数据处理提供了有效参考价值。 相似文献
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目前性能较好的多分类算法有1-v-r支持向量机(SVM)、1-1-1SVM、DDAG SVM等,但存在大量不可分区域且训练时间较长的问题。提出一种基于二叉树的多分类SVM算法用于电子邮件的分类与过滤,通过构建二叉树将多分类转化为二值分类,算法采用先聚类再分类的思想,计算测试样本与子类中心的最大相似度和子类间的分离度,以构造决策节点的最优分类超平面。对于C类分类只需C-1个决策函数,从而可节省训练时间。实验表明,该算法得到了较高的查全率、查准率。 相似文献
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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。 相似文献
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结合特征选择的二叉树SVM多分类算法 总被引:2,自引:0,他引:2
为解决现有二叉树SVM多分类算法采用固定的特征集和结构存在分类精度较低的问题,提出了一种结合特征选择的二又树SVM多类分类算法,采用自上而下分裂的方式构造整个二又树结构,首先计算各节点的所有可能分割,并以分离度和相似度作为依据为各分割选择有效的分类特征子集,再以相应的特征子集计算各分割的类间距,最后选择类间距最大的分割生成子节点,实验结果表明,该算法分类精度较高且计算复杂度低. 相似文献
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Support vector machines (SVMs) have been demonstrated very efficient for binary classification problems; however, computationally efficient and effective multiclass SVMs are still missing. Most existing multiclass SVM classifiers are constructed either by combining multiple binary SVM classifiers, which often perform moderately for some problems, or by converting multiclass problems into one single optimization problem, which is unfortunately computationally expensive. To address these issues, a novel and principled multiclass SVM based on geometric properties of hyperspheres, termed SVMGH, is proposed in this paper. Different from existing SVM‐based methods that seek a cutting hyperplane between two classes, SVMGH draws the discriminative information of each class by constructing a minimum hypersphere containing all class members, and then defines a label function based on the geometric properties of the minimum hyperspheres. We prove theoretically the geometric properties of the minimum hyperspheres to guarantee the validation of SVMGH. The computational efficiency is enhanced by a data reduction strategy as well as a fast training method. Experimental results demonstrate that the proposed SVMGH shows better performance and higher computational efficiency than the state of the art on multiclassification problems while maintaining comparable performance and efficiency on binary classification problems. 相似文献
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Adaptive binary tree for fast SVM multiclass classification 总被引:1,自引:0,他引:1
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. 相似文献
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一种新的基于二叉树的SVM多类分类方法 总被引:25,自引:0,他引:25
介绍了几种常用的支持向量机多类分类方法,分析其存在的问题及缺点。提出了一种基于二叉树的支持向量机多类分类方法(BT SVM),并将基于核的自组织映射引入进行聚类。结果表明,采用该方法进行多类分类比1 v r SVMs和1 v 1 SVMs具有更高的分类精度。 相似文献
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We present an improved version of One-Against-All (OAA) method for multiclass SVM classification based on a decision tree
approach. The proposed decision tree based OAA (DT-OAA) is aimed at increasing the classification speed of OAA by using posterior
probability estimates of binary SVM outputs. DT-OAA decreases the average number of binary SVM tests required in testing phase
to a greater extent when compared to OAA and other multiclass SVM methods. For a balanced multiclass dataset with K classes, under best situation, DT-OAA requires only (K + 1)/2 binary tests on an average as opposed to K binary tests in OAA; however, on imbalanced multiclass datasets we observed DT-OAA to be much faster with proper selection
of order in which the binary SVMs are arranged in the decision tree. Computational comparisons on publicly available datasets
indicate that the proposed method can achieve almost the same classification accuracy as that of OAA, but is much faster in
decision making. 相似文献
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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。 相似文献
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A comparison of methods for multiclass support vector machines 总被引:126,自引:0,他引:126
Chih-Wei Hsu Chih-Jen Lin 《Neural Networks, IEEE Transactions on》2002,13(2):415-425
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. 相似文献
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