Dual unification of bi-class support vector machine formulations

Support vector machine (SVM) theory was originally developed on the basis of a linearly separable binary classification problem, and other approaches have been later introduced for this problem. In this paper it is demonstrated that all these approaches admit the same dual problem formulation in the linearly separable case and that all the solutions are equivalent. For the non-linearly separable case, all the approaches can also be formulated as a unique dual optimization problem, however, their solutions are not equivalent. Discussions and remarks in the article point to an in-depth comparison between SVM formulations and associated parameters.     

Remove Redundancy Samples for SVM in A Chinese Word Segmentation Task

This paper proposes an algorithm that can remove a large number of redundancy samples in a task of using SVM for Chinese word segmentation, and it will not drop much of the final experimental performance. This can ease the training of Chinese word segmentation to a certain extent. This algorithm is fast and needs no extra cost, Both theoretical analysis and experiments show that this algorithm works better, it removes almost 45% of the redundancy samples and the precision ration of our Chinese word segmentation drops less than 3%.     

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SVM的多类别轨迹的分类方法

轨迹分析是解决视觉监控系统中异常检测问题的重要途径.文章将对轨迹进行采样得到的坐标点集作为特征向量,利用SVM训练分类器,并采用一对一算法实现多类别轨迹的分类.实验结果表明,该方法能够满足SVM中核函数对于输入数据的要求,并实现对多类别轨迹的有效分类.     

基于SVM的中文文本自动分类研究

详细介绍了进行文本分类的过程,并着重介绍了一种新的基于结构风险最小化理论的分类算法——支持向量机,通过实验比较支持向量机算法和传统的KNN算法应用于文本分类的效果,证实了支持向量机在处理文本分类问题上的优越性。     

基于SVM的新多类分类算法

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。     

Personalized mode transductive spanning SVM classification tree

Personalized transductive learning (PTL) builds a unique local model for classification of individual test samples and is therefore practically neighborhood dependant; i.e. a specific model is built in a subspace spanned by a set of samples adjacent to the test sample. While existing PTL methods usually define the neighborhood by a predefined (dis)similarity measure, this paper introduces a new concept of a knowledgeable neighborhood and a transductive Support Vector Machine (SVM) classification tree (t-SVMT) for PTL. The neighborhood of a test sample is constructed over the classification knowledge modelled by regional SVMs, and a set of such SVMs adjacent to the test sample is systematically aggregated into a t-SVMT. Compared to a regular SVM and other SVMTs, a t-SVMT, by virtue of the aggregation of SVMs, has an inherent superiority in classifying class-imbalanced datasets. The t-SVMT has also solved the over-fitting problem of all previous SVMTs since it aggregates neighborhood knowledge and thus significantly reduces the size of the SVM tree. The properties of the t-SVMT are evaluated through experiments on a synthetic dataset, eight bench-mark cancer diagnosis datasets, as well as a case study of face membership authentication.     

基于SVM的新多类分类算法

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.     

图像语义分类的树结构SVM方法

为了减小低层视觉特征和高层语义之间存在的"语义鸿沟",提出一种采用树结构支持向量机实现图像底层视觉特征到高层语义的映射方法。利用二叉树结构构建支持向量机(SVM),在SVM核函数空间利用距离作为树节点处的分类度量。二叉树的结构可以大大减小语义分类的时间,而将距离较大的语义类先分离开保证了语义分类具有较高的准确率。实验证明,该方法在保证准确率的同时可以在较大程度上缩短分类检索时间。     

基于SVM分类问题的核函数选择仿真研究

首先讨论支持向量机(SVM)的基本思想和实现过程,随后着重对SVM核函数进行探讨,从理论上研究常用核函数的选择优化问题。采用UCI数据库中的玻璃识别数据、菖蒲植物数据以及汽车评估数据分别对选择不同的核函数情况进行实验仿真分类和比较。仿真结果表明,同类数据选择不同核函数会产生不同的分类效果,选取合适的核函数对分类效果有很大的影响。