一种改善的基于支持向量机的边缘检测算子

支持向量机是一种新的机器学习方法。它建立在统计学习理论基础上,较好地解决了小样本的学习问题。由于其出色的学习性能,该技术已经成为当前国际机器学习界的研究热点。文中提出了一种基于支持向量机的图像边缘检测新方法。这种方法介绍了如何使用支持向量机来高效的检测图像的边缘。首先用几个边缘简单的图像对支持向量机进行训练,然后使用支持向量分类方法进行边缘检测。针对实际图像的边缘检测实验表明,支持向量机可以有效地进行图像的边缘检测,其检测效果和传统的Canny边缘检测算子相当。     

基于向量余弦的支持向量机主动学习策略

针对传统基于主动学习的支持向量机（support vector machine,SVM）方法中所采用的欧式距离不能有效衡量高维样本之间的相关程度,导致学习器泛化能力下降的问题,提出了一种基于向量余弦的支持向量机主动学习（SVM active learning based on vector cosine）策略,称为COS_SVMactive方法。该方法通过在主动学习过程中引入向量余弦来度量训练集中样本信息的冗余度,以挑选那些含有重要分类信息的最有价值样本交给专家进行人工标注,并在迭代的样本标注过程中对训练集的平衡度进行逐步调整,使学习器获得更好的泛化性能。实验结果表明,与传统基于随机采样的SVM主动学习方法（SVM active learning based on ran-dom sampling,RS_SVMactive）和基于距离的SVM主动学习方法（SVM active learning based on distance, DIS_SVMactive）相比,COS_SVMactive方法不仅可以提高分类精度,而且能够减少专家标记代价。     

基于SVM-KNN的半监督托攻击检测方法

针对支持向量机方法在标记用户数据不充分的情况下无法有效实现托攻击检测的不足,提出一种基于SVM-KNN的半监督托攻击检测方法。根据少量标记用户数据训练一个初始SVM分类器,利用初始SVM对大量未标记用户数据进行分类,挑选出分类边界附近有可能成为支持向量的样本点,利用KNN分类器优化边界向量的标记质量,再将重新标注过的边界向量融入训练集,迭代训练逐步改善SVM的分类边界,最终获得系统决策函数。实验结果表明在标记用户数据较少的情况下,方法能有效提高托攻击的检测精度和效率,具有较强的推广能力。     

一种人体跌倒检测方法

随着人口老龄化问题日趋严重,针对老年人容易跌倒的社会问题,进行跌倒检测方法的研究.采用基于穿戴式设备的跌倒检测方法,不同于绝大多数的跌倒事后检测方法,结合加速度特征和角度特征,采用支持向量机算法作为分类算法,进行人体跌倒的事前检测.通过实验发现,跌倒行为的检测率达到99.2%,日常活动行为的检测率达到96%,跌倒检测的平均前置时间为273ms.     

基于躯干检测的单人不良图片识别

目的 互联网中色情图片传播泛滥,对其自动识别与过滤越来越重要,而目前多数不良图片识别方法对类肤色区域较多的正常图像容易产生误检。为此,针对网络上常见的单人色情写真类图片,在总结已有方法不足的基础上提出一种将躯干部位作为感兴趣区域的不良图片识别算法。方法 首先使用基于Poselet(姿态部件)的人体躯干检测方法定位出与色情信息密切相关的躯干区域,然后基于躯干区域提取具有判别力的Fisher向量,最后使用线性支持向量机(SVM)进行分类。然而,由于人体外观变化很大,躯干检测器输出的置信度最大的位置往往较躯干真实的位置有一定的偏移。为了克服这一缺点,提出一种自适应的算法,即根据躯干检测器输出的置信度自适应地选择多个躯干候选区域,并通过集成多个区域的判别结果来得到最终结果。此外,为了训练基于躯干的SVM分类器和验证算法的有效性,本文通过互联网下载的方式收集了一个包含30000幅单人色情写真图片的大规模数据集,并对色情部位进行了标注,标注信息可用于自动生成训练数据。结果 本文提出的基于躯干的自适应分类算法在收集的大规模数据集上达到了91.7%的识别精度,明显高于传统肤色模型的识别结果,尤其是对于如同泳装模特等皮肤裸露较多或类肤色区域较多的图像,本文方法效果尤为显著。结论 文中基于Poselet的躯干检测能够获取与色情信息更相关的信息,因而相比较于传统方法,在较为准确地检测不良图片的同时,有效地降低皮肤裸露较多的正常图像的误检率,达到了实际应用的要求。     

基于遗传算法优化SVM的嵌入式网络系统异常入侵检测

由于传统嵌入式网络系统入侵检测方法难以获得较高的检测精度,提出基于遗传算法优化的支持向量机(GA-SVM)的网络入侵检测技术.支持向量机分类器能够较好地解决少样本、高维、非线性分类问题.然而,支持向量机训练参数的选择对其分类精度有着很大影响,遗传算法能够同时优化支持向量机的训练参数,采用遗传算法进行支持向量机的训练参数同步优化.实验结果表明,这种遗传算法优化的支持向量机分类入侵检测模型有着很高的检测精度.     

An adaptive error penalization method for training an efficient and generalized SVM

A novel training method has been proposed for increasing efficiency and generalization of support vector machine (SVM). The efficiency of SVM in classification is directly determined by the number of the support vectors used, which is often huge in the complicated classification problem in order to represent a highly convoluted separation hypersurface for better nonlinear classification. However, the separation hypersurface of SVM might be unnecessarily over-convoluted around extreme outliers, as these outliers can easily dominate the objective function of SVM. This situation eventually affects the efficiency and generalization of SVM in classifying unseen testing samples. To avoid this problem, we propose a novel objective function for SVM, i.e., an adaptive penalty term is designed to suppress the effects of extreme outliers, thus simplifying the separation hypersurface and increasing the classification efficiency. Since maximization of the margin distance of hypersurface is no longer dominated by those extreme outliers, our generated SVM tends to have a wider margin, i.e., better generalization ability. Importantly, as our designed objective function can be reformulated as a dual problem, similar to that of standard SVM, any existing SVM training algorithm can be borrowed for the training of our proposed SVM. The performances of our method have been extensively tested on the UCI machine learning repository, as well as a real clinical problem, i.e., tissue classification in prostate ultrasound images. Experimental results show that our method is able to simultaneously increase the classification efficiency and the generalization ability of the SVM.     

基于两阶段学习的半监督支持向量机分类算法

提出了一种基于两阶段学习的半监督支持向量机(semi-supervised SVM)分类算法.首先使用基于图的标签传递算法给未标识样本赋予初始伪标识,并利用k近邻图将可能的噪声样本点识别出来并剔除;然后将去噪处理后的样本集视为已标识样本集输入到支持向量机(SVM)中,使得SVM在训练时能兼顾整个样本集的信息,从而提高SVM的分类准确率.实验结果证明,同其它半监督学习算法相比较,本文算法在标识的训练样本较少的情况下,分类性能有所提高且具有较高的可靠性.     

Human Face Detection in Digital Video Using SVMEnsemble

This Letter proposes automatic human face detection in digital video using a support vector machine (SVM) ensemble to improve the detection performance. The SVM ensemble consists of several independently trained SVMs using randomly chosen training samples via a bootstrap technique. Next, they are aggregated in order to make a collective decision via a majority voting scheme. Experimental results show that the proposed face detection method using SVM ensemble outperforms conventional methods such as using only single SVM and Multi-Layer Perceptron in terms of classification accuracy, false alarms, and missing rates.     

基于支持向量机的目标检测算法综述

目标检测的目的在于从静态图片或视频中检测并定位设定种类的目标物体, 已有研究大都将目标检测问题简化为一个二分类问题. 鉴于支持向量机在模式识别领域尤其是解决二分类问题中所表现出来的优越性, 如何将其应用于目标检测已成为当今计算机视觉领域关注的重点. 对此, 从支持向量机原理、目标特征模型构建、学习训练和目标检测框确定等角度, 综述了基于支持向量机的目标检测算法的研究现状, 并就进一步的发展进行了展望.     

融合特征选取和学习策略的支持向量机研究

支持向量机是重要的机器学习方法之一,已成功解决了许多实际的分类问题。围绕如何提高支持向量机的分类精度与训练效率,以分类过程为主线,主要综述了在训练支持向量机之前不同的特征选取方法与学习策略。在此基础上,比较了不同的特征选取方法SFS,IWSS,IWSSr以及BARS的分类精度,分析了主动学习策略与支持向量机融合后获得的分类器在测试集上的分类精度与正确率/召回率平衡点两个性能指标。实验结果表明,包装方法与过滤方法相结合的特征选取方法能有效提高支持向量机的分类精度和减少训练样本量;在标签数据较少的情况下,主动学习能达到更好的分类精度,而为了达到相同的分类精度,被动学习需要的样本数量必须要达到主动学习的6倍。     

Pedestrian detection for intelligent transportation systems combining AdaBoost algorithm and support vector machine

Pedestrians are the vulnerable participants in transportation system when crashes happen. It is important to detect pedestrian efficiently and accurately in many computer vision applications, such as intelligent transportation systems (ITSs) and safety driving assistant systems (SDASs). This paper proposes a two-stage pedestrian detection method based on machine vision. In the first stage, AdaBoost algorithm and cascading method are adopted to segment pedestrian candidates from image. To confirm whether each candidate is pedestrian or not, a second stage is needed to eliminate some false positives. In this stage, a pedestrian recognizing classifier is trained with support vector machine (SVM). The input features used for SVM training are extracted from both the sample gray images and edge images. Finally, the performance of the proposed pedestrian detection method is tested with real-world data. Results show that the performance is better than conventional single-stage classifier, such as AdaBoost based or SVM based classifier.     

基于仿射聚类的主动SVM多类分类方法

针对现有的主动学习算法在多分类器应用中存在准确率低、速度慢等问题,将基于仿射传播(AP)聚类的主动学习算法引入到多分类支持向量机中,每次迭代主动选择最有利于改善多类SVM分类器性能的N个新样本点添加到训练样本点中进行学习,使得在花费较小标注代价情况下,能够获得较高的分类性能。在多个不同数据集上的实验结果表明,新方法能够有效地减少分类器训练时所需的人工标注样本点的数量,并获得较高的准确率和较好的鲁棒性。     

Fuzzy SVM for content-based image retrieval: a pseudo-label support vector machine framework

Conventional relevance feedback in content-based image retrieval (CBIR) systems uses only the labeled images for learning. Image labeling, however, is a time-consuming task and users are often unwilling to label too many images during the feedback process. This gives rise to the small sample problem where learning from a small number of training samples restricts the retrieval performance. To address this problem, we propose a technique based on the concept of pseudo-labeling in order to enlarge the training data set. As the name implies, a pseudo-labeled image is an image not labeled explicitly by the users, but estimated using a fuzzy rule. Therefore, it contains a certain degree of uncertainty or fuzziness in its class information. Fuzzy support vector machine (FSVM), an extended version of SVM, takes into account the fuzzy nature of some training samples during its training. In order to exploit the advantages of pseudo-labeling, active learning and the structure of FSVM, we develop a unified framework called pseudo-label fuzzy support vector machine (PLFSVM) to perform content-based image retrieval. Experimental results based on a database of 10,000 images demonstrate the effectiveness of the proposed method.     

利用粗糙集理论提高SVM预测系统的实时性

支持向量机是一种新的机器学习方法,它具有良好的推广性和分类精确性。但是在利用支持向量机的分类算法处理实际问题时,该算法的计算速度较慢、处理问题效率较低。文中介绍了一种新的学习算法,就是将粗糙集和支持向量机相结合,利用粗糙集对支持向量机的训练样本进行预处理,从而缩短样本的训练时间,提高基于SVM预测系统实时性。文中最后利用该方法进行了数据试验,试验结果表明了该方法可以大大缩短样本的训练时间,提高基于支持向量机处理预测系统的效率。从而也证明了该方法的有效性。     

基于支持向量机的高速公路事件检测

提出用支持向量机分类方法研究高速公路事件检测问题。阐述了支持向量机分类算法,根据交通事件对交通流参数的影响规律选择了支持向量机的输入量,用高速公路管理处提供的样本数据进行了仿真研究。仿真实验表明,支持向量机事件检测算法具有检测准确率高、训练时间短、泛化能力好等优点,它为事件检测提供了一种切实可行的新思路。     

结合Tri-training半监督学习和凸壳向量的SVM主动学习算法*

为解决监督学习过程中难以获得大量带有类标记样本且样本数据标记代价较高的问题,结合主动学习和半监督学习方法,提出基于Tri-training半监督学习和凸壳向量的SVM主动学习算法.通过计算样本集的壳向量,选择最有可能成为支持向量的壳向量进行标记.为解决以往主动学习算法在选择最富有信息量的样本标记后,不再进一步利用未标记样本的问题,将Tri-training半监督学习方法引入SVM主动学习过程,选择类标记置信度高的未标记样本加入训练样本集,利用未标记样本集中有利于学习器的信息.在UCI数据集上的实验表明,文中算法在标记样本较少时获得分类准确率较高和泛化性能较好的SVM分类器,降低SVM训练学习的样本标记代价.     

A Novel Active Learning Method Using SVM for Text Classification

Support vector machines (SVMs) are a popular class of supervised learning algorithms, and are particularly applicable to large and high-dimensional classification problems. Like most machine learning methods for data classification and information retrieval, they require manually labeled data samples in the training stage. However, manual labeling is a time consuming and errorprone task. One possible solution to this issue is to exploit the large number of unlabeled samples that are easily accessible via the internet. This paper presents a novel active learning method for text categorization. The main objective of active learning is to reduce the labeling effort, without compromising the accuracy of classification, by intelligently selecting which samples should be labeled. The proposed method selects a batch of informative samples using the posterior probabilities provided by a set of multi-class SVM classifiers, and these samples are then manually labeled by an expert. Experimental results indicate that the proposed active learning method significantly reduces the labeling effort, while simultaneously enhancing the classification accuracy.     

基于支持向量机和距离度量的纹理分类

针对图象纹理分类问题，提出了一种将支持向量机和距离度量相结合，以构成两级组合分类器的分类方法，用该方法分类时，先采用距离度量进行前级分类，然后根据图象的纹理统计特征，采用欧氏距离来度量图象之间的相似性，若符合条件，则给出分类结果，否则拒识，并转入后级分类器，而后级分类器则采用一种新的模式分类方法－支持向量机进行分类，该组合分类方法不仅充分利用了支持向量机识别率高和距离度量速度快的优点，并且还利用距离度量的结果去指导支持向量机的训练和测试，由纹理图象分类的实验表明，该算法具有较高的效率和识别精度，同时也对推动支持向量机这一新的模式分类方法的实际应用具有积极意义。     

基于机器学习方法的直流电弧故障检测

为了解决传统分析方法在直流供电系统中电弧故障检测的精确度不足及过程繁琐的问题,将直流电弧故障检测归为二分类问题,引入机器学习方法,通过直流电弧实验得到正常状态和电弧状态的数据,从时域中提取电流均值等4个特征,从频域中提取高频分量标准差等3个特征.利用提取到的特征对支持向量机(SVM)进行训练,利用求解得到的模型对测试数据集进行分类,分类准确率为94.483%.结果证明:所提方法能有效检测直流电弧故障,提高故障检测精度,且步骤精简,易于推广.