一种改进的支持向量机多类分类方法

提出一种新的基于二叉树结构的支持向量机(SVM)多类分类方法.该方法解决了现有主要算法中存在的不可分区域问题,具有简单、直观、重复训练样本少的优点.为了提高分类模型的推广能力,必须使样本分布好的类处于二又树的上层节点,才能获得更大的划分空间.因此,该算法采用类间散布度量与类内散布度量的比值作为二叉树的生成算法.采用UCI标准数据集实验,实验结果表明该算法具有一定的优越性.     

Universal Filtering Via Hidden Markov Modeling

The problem of discrete universal filtering, in which the components of a discrete signal emitted by an unknown source and corrupted by a known discrete memoryless channel (DMC) are to be causally estimated, is considered. A family of filters are derived, and are shown to be universally asymptotically optimal in the sense of achieving the optimum filtering performance when the clean signal is stationary, ergodic, and satisfies an additional mild positivity condition. Our schemes are comprised of approximating the noisy signal using a hidden Markov process (HMP) via maximum-likelihood (ML) estimation, followed by the use of the forward recursions for HMP state estimation. It is shown that as the data length increases, and as the number of states in the HMP approximation increases, our family of filters attains the performance of the optimal distribution-dependent filter. An extension to the case of channels with memory is also established.     

一种改进的渐进直推式支持向量机分类学习算法

基于支持向量机的直推式学习是统计学习理论中一个较新的研究领域。较之传统的归纳式学习方法而言,直推式学习往往更具有普遍性和实际意义。针对渐进直推式支持向量机学习算法存在的缺陷,提出了一种改进算法。该算法利用区域标注法取代前者的成对标注法,在继承了其渐进赋值和动态调整的规则的同时,提高了算法的速度;根据每个无标签样本的标注可信度自适应地对其赋予不同的影响因子,从而控制训练误差的传递和积累,提高了算法的性能。雷达实测数据实验结果表明该算法是有效的。     

支持向量机和BP网络改进模型的性能对比研究

通过引入支持向量机（SVM）方法，提出了基于SVM的遥感图像多类分类模型，分析了SVM多类分类器的构造及其参数选取问题，并结合实例，讨论了SVM分类器性能随其本身参数变化情况，最后与几种代表性的BP网络改进模型进行了系统的对比分析。实验表明，SVM方法的分类时间要远大于改进的BP模型，而分类精度优于BP网络改进模型中效果最好的几种优化算法3个百分点左右，是一种有效的图像分类方法。     

Modeling of 3-D Vertical Interconnect Using Support Vector Machine Regression

In this letter, the support vector machine (SVM) regression approach is introduced to model the three-dimensional (3-D) high density microwave packaging structure. The SVM is based on the structural risk minimization principle, which leads to a good generalization ability. With a 3-D vertical interconnect used as an example, the SVM regression model is electromagnetically developed with a set of training data and testing data, which is produced by the electromagnetic simulation. Experimental results suggest that the developed model performs with a good predictive ability in analyzing the electrical performance     

基于改进的一对一支持向量机方法的多目标图像分割

支持向量机方法被看作是对传统学习分类方法的一个好的替代,特别在小样本、高维情况下,具有较好的泛化性能.文章对一对一支持向量机方法进行了改进,并采用其对多目标图像进行了分割研究.实验结果表明,支持向量机方法是一种很有前景的图像分割技术.     

A Robust Hidden Markov Gauss Mixture Vector Quantizer for a Noisy Source

Noise is ubiquitous in real life and changes image acquisition, communication, and processing characteristics in an uncontrolled manner. Gaussian noise and Salt and Pepper noise, in particular, are prevalent in noisy communication channels, camera and scanner sensors, and medical MRI images. It is not unusual for highly sophisticated image processing algorithms developed for clean images to malfunction when used on noisy images. For example, hidden Markov Gauss mixture models (HMGMM) have been shown to perform well in image segmentation applications, but they are quite sensitive to image noise. We propose a modified HMGMM procedure specifically designed to improve performance in the presence of noise. The key feature of the proposed procedure is the adjustment of covariance matrices in Gauss mixture vector quantizer codebooks to minimize an overall minimum discrimination information distortion (MDI). In adjusting covariance matrices, we expand or shrink their elements based on the noisy image. While most results reported in the literature assume a particular noise type, we propose a framework without assuming particular noise characteristics. Without denoising the corrupted source, we apply our method directly to the segmentation of noisy sources. We apply the proposed procedure to the segmentation of aerial images with Salt and Pepper noise and with independent Gaussian noise, and we compare our results with those of the median filter restoration method and the blind deconvolution-based method, respectively. We show that our procedure has better performance than image restoration-based techniques and closely matches to the performance of HMGMM for clean images in terms of both visual segmentation results and error rate.     

Context-Sensitive Hidden Markov Models for Modeling Long-Range Dependencies in Symbol Sequences

The hidden Markov model (HMM) has been widely used in signal processing and digital communication applications. It is well known for its efficiency in modeling short-term dependencies between adjacent symbols. However, it cannot be used for modeling long-range interactions between symbols that are distant from each other. In this paper, we introduce the concept of context-sensitive HMM. The proposed model is capable of modeling strong pairwise correlations between distant symbols. Based on this model, we propose dynamic programming algorithms that can be used for finding the optimal state sequence and for computing the probability of an observed symbol string. Furthermore, we also introduce a parameter re-estimation algorithm, which can be used for optimizing the model parameters based on the given training sequences.     

基于改进的共轭梯度算法实现的最小二乘隐空间支持向量机

本文研究了最小二乘隐空间支持向量机的优化问题．文中采用基于对称超松弛预处理技术改进共轭梯度算法,改进的共轭梯度算法只需求解一个阶数为1-1的线性代数方程组即可,大大节省了计算时间．最后将其应用于最小二乘隐空间支持向量机中建立数学模型,并通过实例验证了该算法的优越性．     

基于改进隐马尔可夫模型的网络动态风险评估

网络风险评估是网络系统安全管理的基础和前提,网络风险评估方法是进行网络风险评估的关键技术,评估方法的选择直接关系到评估的最终结果。传统的网络风险评估方法实时性较差,将隐马尔可夫模型运用于风险评估中,可提高风险评估的实时性。使用改进蚁群算法训练隐马尔可夫模型（HMM）,使训练后的模型具有更高的识别能力。在基于现实数据的实验测试中,该方法得到了较好的检测结果。     

基于支持向量机的关键词拒识算法

支持向量机是统计理论学习中一个重要的学习方法，也是解决模式识别问题的强有力工具，尤其在二元分类上有着突出的优势。拒识技术是语音识别系统走向实用化的关键技术之一，但由于语音信号的复杂性，使得拒识一直是语音识别技术中的难题。有机的将支持向量机技术应用于关键词识别的拒识问题中，把关键词识别中的正识和误识作为支持向量机的二元分类对象。这种方法避免了传统拒识方法对拒识门限的确定，同时充分发挥了支持向量机在二元分类上的优势。实验表明该算法效果较为有效。     

基于支持向量机的模式识别方法

基于统计学习理论的支持向量机(SVM)方法是现代智能技术的一个重要分支。SVM实现了结构风险最小化(SRM),而不是经验风险最小化(ERM),在保证分类精度的前提下,提高了分类器的泛化能力。着重讨论C-SVM原理,并在此基础之上,对算法进行了测试。测试结果表明,C-SVM分类算法具有较好的推广能力。     

基于支持向量机的股票预测

支持向量机(support vector machine SVM)作为一种可训练的机器学习方法,目前已广泛应用于股票预测中。文章利用matlab和支持向量机,结合股票前一段时间的走势数据,做出数据回归拟合,得到训练模型,然后根据训练模型对未来的股票指数做出预测,对预测数据和原始数据做一些误差分析。     

基于支持向量机的图像预测编码算法

提出了一种利用支持向量机（SVM）构建预测器的图像压缩预测编码算法。该算法将遗传算法引入支持向量机中,在自学习的过程中,能够自动调整SVM的核参数,获取准确的图像信息。通过仿真试验,并与传统预测编码方法、神经网络预测编码方法进行比较,结果证明该算法恢复图像效果最好。     

基于支持向量机(SVM)的图像去噪方法

提出了一种基于支持向量机进行图像去噪的方法。该方法利用支持向量回归技术构造图像去噪所需的滤波器．其中特征的提取和训练样本的设计旨在抑制不同类型的噪声。实验结果表明，该方法能够有效去除噪声，并能较好地保护边缘信息，适用于边缘检测等操作的预处理。     

Image Segmentation Based on Support Vector Machine

Image segmentation is a necessary step in image analysis. Support vector machine （SVM） approach is proposed to segment images and its segmentation performance is evaluated. Experimental results show that： the effects of kernel function and model parameters on the segmentation performance are significant; SVM approach is less sensitive to noise in image segmentation; The segmentation performance of SVM approach is better than that of back-propagation multi-layer perceptron （BP-MLP） approach and fuzzy c-means （FCM） approach.     

基于支持向量机的步态识别算法研究

为了准确快速地进行运动人体的步态识别,提出了一种基于主分量分析(PCA)和统一Hu矩融合的步态识别算法。将人体髋关节以下作为感兴趣区域,对图像序列中运动人体的感兴趣区域进行了分割,并提取主分量外形特征,同时计算感兴趣区域的统一Hu不变矩特征,将二者结合,构成步态序列的特征空间,采用支持向量机(SVM)分类器进行分类识别,通过MATLAB仿真实验验证了算法的有效性。实验结果表明,该算法识别速度快,具有较高的识别率。     

基于支持向量机的无人机视觉障碍检测

自主障碍检测与回避是无人机低高度飞行时保障其生存性的一项关键技术,有重要的研究意义。通过对机器视觉原理的研究,考虑到支持向量机方法能同时减小匹配难度和计算量,实时性能、泛化性能良好,故采用该方法通过离线监督学习,将无人机前视图像分割为天空与非天空2部分,并将非天空部分作为需要回避的障碍,实现无人机基于视觉的障碍检测系统,为后续的视觉制导提供信息。实验结果表明,支持向量机能有效准确地实现图像的天空分割,并具有良好的泛化性能。     

适用于不平衡样本数据处理的支持向量机方法

支持向量机算法在处理不平衡样本数据时，其分类器预测具有倾向性．样本数量多的类别，其分类误差小，而样本数量少的类别，其分类误差大．本文针对这种倾向性问题，在分析其产生原因的基础上，提出了基于遗传交叉运算的改进方法．对于小类别训练样本，利用交叉运算产生新的样本，从而补偿了因训练数据类别大小差异而造成的影响．基于UCI标准数据集的仿真实验结果表明，改进方法比标准支持向量机方法具有更好的分类准确率．