双模糊渐进直推式支持向量机算法

直推式支持向量机(TSVM)是支持向量机与直推式学习相结合的重要算法.文中为TSVM中的临时标签样本引入双模糊隶属度以及样本修剪策略,构建一种双模糊渐进直推式支持向量机(BFPTSVM)算法.该算法可有效降低TSVM的计算复杂度及核存储量.模拟实验表明该算法可取得比其他算法更好的分类性能,并且具有较快的收敛速度.     

基于k均值聚类的直推式支持向量机学习算法

针对直推式支持向量机（TSVM）学习模型求解难度大的问题,提出了一种基于k均值聚类的直推式支持向量机学习算法——TSVMKMC。该算法利用k均值聚类算法,将无标签样本分为若干簇,对每一簇样本赋予相同的类别标签,将无标签样本和有标签样本合并进行直推式学习。由于TSVMKMC算法有效地降低了状态空间的规模,因此运行速度较传统算法有了很大的提高。实验结果表明,TSVMSC算法能够以较快的速度达到较高的分类准确率。     

基于改进k近邻的直推式支持向量机学习算法

针对直推式支持向量机(TSVM)需要遍历所有无标签样本花费时间长的缺点,提出一种基于改进k近邻法的直推式支持向量机学习算法--k2TSVM。该算法首先使用k均值聚类将无标签样本分成若干簇,然后求出每簇中心点的k近邻并根据其中正负样本个数对无标签样本进行删减,将删减后的数据集输入直推式支持向量机进行训练。k2TSVM改善传统TSVM需要遍历所有无标签数据的缺点,有效减少训练样本规模,能够提高运行速度。实验结果表明,k2TSVM在降低运行时间的同时,能够取得比类似TSVM改进算法更好的分类结果。      

基于增量学习的直推式支持向量机算法

针对直推式支持向量机在进行大数据量分类时出现精度低、学习速度慢和回溯式学习多的问题,提出了一种基于增量学习的直推式支持向量机分类算法,将增量学习引入直推式支持向量机,使其在训练过程中仅保留有用样本而抛弃无用样本,从而减少学习时间,提高分类速度。实验结果表明,该算法具有较快的分类速度和较高的分类精度。     

近邻渐进直推式支持向量机算法

针对渐进直推式支持向量机箅法训练速度慢和学习性能不稳定的问题,提出一种近邻渐进直推式支持向量机算法.该算法利用支持向量机中支持向量信息,选择支持向量附近的无标签样本点进行标注,采用支持向量预选取的方法减少训练集的规模,提高算法的速度.实验结果表明了该算法的有效性.     

基于SVDD的渐进直推式支持向量机学习算法

针对半监督学习中渐进直推支持向量机(PTSVM)算法每次标注的样本数太少、训练速度慢、回溯式学习多、学习性能不稳定的问题,提出一种快速的渐进直推支持向量机学习算法.该算法利用支持向量的信息,基于支持向量域描述(SVDD)选择新标注、无标签的样本点,以区域标注法代替PTSVM的成对标注法,不仅继承了其渐进赋值和动态调整的规则,而且在保持甚至提高算法精度的同时,大大提高算法速度.在人工模拟数据和真实数据上的实验结果表明该算法的有效性.     

基于直推式支持向量机的图像分类算法

直推式支持向量机(TSVM)是在利用有标签样本的同时，考虑无标签样本对分类器的影响，并且结合支持向量机算法，实现一种高效的分类算法。它在包含少量有标签样本的训练集和大量无标签样本的测试集上，具有良好的效果。但是它有算法时间复杂度比较高，需要预先设置正负例比例等不足。通过对原有算法的改进，新算法在时间复杂度上明显下降，同时算法效果没有明显的影响。     

直推式支持向量机在Web信息抽取中的应用研究

直推式支持向量机是一种直接从已知样本出发对特定的未知样本进行识别的分类技术。在分析直推式支持向量机分类原理的基础上,提出一种基于直推式支持向量机的Web信息抽取方法,直接从分类的角度抽取Web信息。只需要提供少量标记样本就可以实现对大量未标注样本的分类标注,从而以分类的方式完成Web数据抽取任务。实验结果表明,使用这种方法进行Web信息抽取是有效性。     

主动学习与半监督技术相结合的海冰图像分类

针对海冰遥感图像分类问题中标签样本获取困难、标注成本较高导致海冰分类精度难以提高的问题,提出了一种主动学习与半监督学习相结合的方式用于海冰分类。首先,利用基于不确定性准则和多样性准则进行主动学习方法,选择一批最具信息量的标签样本建立标签样本集;其次,充分利用大量的未标签样本信息,并融合主动学习采样的思想选出部分具有代表性且分布在支持向量周边的半标签样本,建立半监督分类模型;最后,将主动学习方法和直推式支持向量机相结合构建分类模型实现海冰图像分类。实验结果表明,相对于其他方法,该方法在只有少量标签样本的情况下,可以获得更高的分类精度,该方式可有效解决遥感海冰分类问题。     

Least Square Transduction Support Vector Machine

Support vector machine (SVM) is a general and powerful learning machine, which adopts supervised manner. However, for many practical machine learning and data mining applications, unlabeled training examples are readily available but labeled ones are very expensive to be obtained. Therefore, semi-supervised learning emerges as the times require. At present, the combination of SVM and semi-supervised learning principle such as transductive learning has attracted more and more attentions. Transductive support vector machine (TSVM) learns a large margin hyperplane classifier using labeled training data, but simultaneously force this hyperplane to be far away from the unlabeled data. TSVM might seem to be the perfect semi-supervised algorithm since it combines the powerful regularization of SVMs and a direct implementation of the clustering assumption, nevertheless its objective function is non-convex and then it is difficult to be optimized. This paper aims to solve this difficult problem. We apply least square support vector machine to implement TSVM, which can ensure that the objective function is convex and the optimization solution can then be easily found by solving a set of linear equations. Simulation results demonstrate that the proposed method can exploit unlabeled data to yield good performance effectively.     

A bi-fuzzy progressive transductive support vector machine(BFPTSVM) algorithm

Transductive support vector machine (TSVM) is a well-known algorithm that realizes transductive learning in the field of support vector classification. This paper constructs a bi-fuzzy progressive transductive support vector machine (BFPTSVM) algorithm by combining the proposed notation of bi-fuzzy memberships for the temporary labeled sample appeared in progressive learning process and the sample-pruning strategy, which decreases the computation complexity and store memory of algorithm. Simulation experiments show that the BFPTSVM algorithm derives better classification performance and converges rapidly with better stability compared to the other learning algorithms.     

基于直推式支持向量机的图像检索

为缩小图像的低层特征与高层语义之间的语义鸿沟,基于支持向量机的相关反馈机制受到越来越广泛的关注,但这种方法并没有利用未标记样本的隐含信息.为更好地利用这些信息,提出将直推式支持向量机作为反馈过程中的学习算法.通过分析其所用特征向量的特点,设计一种颜色稀疏特征,并将其与纹理特征结合作为图像描述的特征.实验结果表明该方法较令人满意,同时也说明直推式支持向量机可在文本分类以外的领域取得较好结果.     

Robust energy-based least squares twin support vector machines

Twin support vector machine (TSVM), least squares TSVM (LSTSVM) and energy-based LSTSVM (ELS-TSVM) satisfy only empirical risk minimization principle. Moreover, the matrices in their formulations are always positive semi-definite. To overcome these problems, we propose in this paper a robust energy-based least squares twin support vector machine algorithm, called RELS-TSVM for short. Unlike TSVM, LSTSVM and ELS-TSVM, our RELS-TSVM maximizes the margin with a positive definite matrix formulation and implements the structural risk minimization principle which embodies the marrow of statistical learning theory. Furthermore, RELS-TSVM utilizes energy parameters to reduce the effect of noise and outliers. Experimental results on several synthetic and real-world benchmark datasets show that RELS-TSVM not only yields better classification performance but also has a lower training time compared to ELS-TSVM, LSPTSVM, LSTSVM, TBSVM and TSVM.     

Building sparse twin support vector machine classifiers in primal space

Twin support vector machines (TSVM) obtain faster training speeds than classical support vector machines (SVM). However, TSVM augmented vectors lose sparsity. In this paper, a rapid sparse twin support vector machine (STSVM) classifier in primal space is proposed to improve the sparsity and robustness of TSVM. Based on a simple back-fitting strategy, the STSVM iteratively builds each nonparallel hyperplanes by adding one support vector (SV) from the corresponding class at one time. This process is terminated using an adaptive and stable stopping criterion. STSVM learning is implemented by linear equation computing systems through introducing a quadratic function to approximate the empirical risk. The computational results on several synthetic and benchmark datasets indicate that the STSVM obtains a sparse separating hyperplane at a low cost without sacrificing its generalization performance.     

核聚类启发式转导SVM

传统转导支持向量机有效地利用了未标记样本,具有较高的分类准确率,但是计算复杂度较高。针对该不足,论文提出了一种基于核聚类的启发式转导支持向量机学习算法。首先将未标记样本利用核聚类算法进行划分,然后对划分后的每一簇样本标记为同一类别,最后根据传统的转导支持向量机算法进行新样本集合上的分类学习。所提方法通过对核聚类后同一簇未标记样本赋予同样的类别,极大地降低了传统转导支持向量机算法的计算复杂度。在MNIST手写阿拉伯数字识别数据集上的实验表明,所提算法较好地保持了传统转导支持向量机分类精度高的优势。     

KNN-based least squares twin support vector machine for pattern classification

The least squares twin support vector machine (LSTSVM) generates two non-parallel hyperplanes by directly solving a pair of linear equations as opposed to solving two quadratic programming problems (QPPs) in the conventional twin support vector machine (TSVM), which makes learning speed of LSTSVM faster than that of the TSVM. However, LSTSVM fails to discover underlying similarity information within samples which may be important for classification performance. To address the above problem, we apply the similarity information of samples into LSTSVM to build a novel non-parallel plane classifier, called K-nearest neighbor based least squares twin support vector machine (KNN-LSTSVM). The proposed method not only retains the superior advantage of LSTSVM which is simple and fast algorithm but also incorporates the inter-class and intra-class graphs into the model to improve classification accuracy and generalization ability. The experimental results on several synthetic as well as benchmark datasets demonstrate the efficiency of our proposed method. Finally, we further went on to investigate the effectiveness of our classifier for human action recognition application.     

基于半监督多示例学习的对象图像检索

针对基于对象的图像检索问题,提出一种新的半监督多示例学习(MIL)算法.该算法将图像当作包,分割区域的视觉特征当作包中的示例,按"点密度"最大原则,提取"视觉语义"构造投影空间;然后利用定义的非线性函数将包映射成投影空间中的一个点,以获得图像的"投影特征",并采用粗糙集(RS)方法对其进行属性约简;最后利用直推式支持向量机(TSVM)进行半监督的学习,得到分类器.实验结果表明,该方法有效且性能优于其他方法.