Applications of support vector machines to speech recognition

Recent work in machine learning has focused on models, such as the support vector machine (SVM), that automatically control generalization and parameterization as part of the overall optimization process. In this paper, we show that SVMs provide a significant improvement in performance on a static pattern classification task based on the Deterding vowel data. We also describe an application of SVMs to large vocabulary speech recognition and demonstrate an improvement in error rate on a continuous alphadigit task (OGI Alphadigits) and a large vocabulary conversational speech task (Switchboard). Issues related to the development and optimization of an SVM/HMM hybrid system are discussed.     

基于支持向量机的异常值检测的两种方法

支持向量机逐渐成为机器学习的一种方法。异常值检测是支持向量机中一种特殊的分类问题,被称为一类分类。一类分类通过核映射确定一个包含正类样本的紧致区域,以便使异常值更容易暴露出来。介绍了一些一类分类算法的基本思想。     

一种针对基于SVM入侵检测系统的毒性攻击方法

在机器学习被广泛应用的背景下,本文提出一种针对基于SVM（Support Vector Machine）入侵检测系统的新颖攻击方法——毒性攻击.该方法通过篡改训练数据,进而误导SVM的机器学习过程,降低入侵检测系统的分类模型对攻击流量的识别率.本文把这种攻击建模为最优化问题,利用数值方法得到攻击样本.通过包含多种攻击类型的NSL-KDD数据集进行实验,从攻击流量的召回率和精度这两个指标对攻击效果进行评估,与已有方法相比,实验结果表明本文方法可更有效地降低入侵检测系统的识别率.本文希望通过该研究进一步认识针对机器学习的新颖攻击,为下一步研究对应的防御机制提供研究基础.     

支持向量机中的模型选择研究

支持向量机是一种新型的机器学习方法。模型选择是设计支持向量机的关键。本文在分析用于分类的支持向量机原理的基础上,分别从核函数类型和核参数的选择等模型选择方面进行了探讨。最后在上述理论分析的基础上进行了实验,取得了较好的效果。     

一种改进的支持向量机模式分类方法

支持向量机(Support Vector Machine,SVM)是在统计学习理论基础上发展起来的一种新的机器学习方法,已成为目前研究的热点,并在模式识别领域有了广泛的应用.首先分析了支持向量机原理,随后引入一种改进的径向基核函数,在此基础上,提出了一种改进核函数的SVM模式分类方法.与基于IRIS数据,进行了计算机仿真实验,与基干模糊k-近邻的模式分类仿真结果比较,结果表明改进的SVM方法分类性能比模糊k-近邻算法(Fuzzy k-Nearest Neighbor,FKNN)的分类性能更好,运算时间更短,更易于实时实现.     

DDoS detection based on wavelet kernel support vector machine

To enhance the detection accuracy and deduce false positive rate of distributed denial of service （DDoS） attack detection, a new machine learning method was proposed. With the analysis of support vector machine （SVM） and the wavelet kernel function theory, an admissive support vector kernel, which is a wavelet kernel constructed in this article, implements the combination of the wavelet technique with SVM. Then, wavelet support vector machine （WSVM） is applied to DDoS attack detections and as a classifying means to test the validity of the wavelet kernel function. Simulation experiments show that under the same conditions, the predictive ability of WSVM is improved and the computation burden is alleviated. The detection accuracy of WSVM is higher than the traditional SVM by about 4%, while its false positive is lower than the traditional SVM. Thus, for DDoS detections, WSVM shows better detection performance and is more adaptive to the changing network environment.     

基于混合核的PSO-SVM的L形天线谐振频率建模

人工神经网络进行建模时通常需要准备大量的数据样本,同时,网络结构一般都比较复杂;而采用支持向量机（SVM）进行建模时,不同核函数有不同的效果,各有利弊,且选取SVM模型参数的理论支撑尚不完整。为了解决这些问题,文中提出了一种基于粒子群优化（PSO）算法的新的SVM混合核函数,这种混合核函数是将局部核函数中的柯西核函数和全局核函数中的多项式核函数进行线性组合,且组合系数和各个核函数中的参数采用PSO算法来优化选取。采用UCI数据库中的wine-red数据集对该混合核函数进行了验证,仿真结果表明,该混合核函数可以提高模型的学习能力和泛化能力。最后,将基于混合核函数的PSO SVM方法用于L形微带天线谐振频率建模,进一步证明了这种方法是可行的和有效的。     

基于支持向量机的说话人辨认研究

支持向量机是统计学理论的一个重要的学习方法，也是解决模式识别问题的一个有力的工具，本文提出了用支持向量机来解决说话人辨认问题。结合语音信号的特点，解决了大数据量情况下支持向量机的训练问题。支持向量机对两类的分类问题有着突出的优势，本文用两种判决规则将两类问题应用到多类的识别问题。用支持向量机实现了一个与文本无关的说话人辨认系统，实验表明，本方法有良好的效果。     

An accurate traffic classification model based on support vector machines

Network traffic classification is a fundamental research topic on high‐performance network protocol design and network operation management. Compared with other state‐of‐the‐art studies done on the network traffic classification, machine learning (ML) methods are more flexible and intelligent, which can automatically search for and describe useful structural patterns in a supplied traffic dataset. As a typical ML method, support vector machines (SVMs) based on statistical theory has high classification accuracy and stability. However, the performance of SVM classifier can be severely affected by the data scale, feature dimension, and parameters of the classifier. In this paper, a real‐time accurate SVM training model named SPP‐SVM is proposed. An SPP‐SVM is deducted from the scaling dataset and employs principal component analysis (PCA) to extract data features and verify its relevant traffic features obtained from PCA. By employing PCA algorithm to do the dimension extraction, SPP‐SVM confirms the critical component features, reduces the redundancy among them, and lowers the original feature dimension so as to reduce the over fitting and increase its generalization effectively. The optimal working parameters of kernel function used in SPP‐SVM are derived automatically from improved particle swarm optimization algorithm, which will optimize the global solution and make its inertia weight coefficient adaptive without searching for the parameters in a wide range, traversing all the parameter points in the grid and adjusting steps gradually. The performance of its two‐ and multi‐class classifiers is proved over 2 sets of traffic traces, coming from different topological points on the Internet. Experiments show that the SPP‐SVM's two‐ and multi‐class classifiers are superior to the typical supervised ML algorithms and performs significantly better than traditional SVM in classification accuracy, dimension, and elapsed time.     

Classification of hyperspectral remote sensing images with support vector machines

This paper addresses the problem of the classification of hyperspectral remote sensing images by support vector machines (SVMs). First, we propose a theoretical discussion and experimental analysis aimed at understanding and assessing the potentialities of SVM classifiers in hyperdimensional feature spaces. Then, we assess the effectiveness of SVMs with respect to conventional feature-reduction-based approaches and their performances in hypersubspaces of various dimensionalities. To sustain such an analysis, the performances of SVMs are compared with those of two other nonparametric classifiers (i.e., radial basis function neural networks and the K-nearest neighbor classifier). Finally, we study the potentially critical issue of applying binary SVMs to multiclass problems in hyperspectral data. In particular, four different multiclass strategies are analyzed and compared: the one-against-all, the one-against-one, and two hierarchical tree-based strategies. Different performance indicators have been used to support our experimental studies in a detailed and accurate way, i.e., the classification accuracy, the computational time, the stability to parameter setting, and the complexity of the multiclass architecture. The results obtained on a real Airborne Visible/Infrared Imaging Spectroradiometer hyperspectral dataset allow to conclude that, whatever the multiclass strategy adopted, SVMs are a valid and effective alternative to conventional pattern recognition approaches (feature-reduction procedures combined with a classification method) for the classification of hyperspectral remote sensing data.     

凸组合核函数的支持向量机高光谱图像分类

支持向量机的高光谱图像分类中,单核函数存在局限性。为了提高分类器的分类精度和支持向量机模型的泛化能力,利用高斯径向基核和多层感知核进行凸组合构造复合核函数支持向量机,证明了该函数满足作为核函数的判决Mercer条件,并进一步将凸组合核函数支持向量机应用到高光谱图像分类中,完成了建模和实验验证。实验结果表明,凸组合核函数具有较好的鲁棒性,且该类支持向量机的分类精度和KAPPA系数较单核SVM均得到了有效的提高,是一种解决多分类问题行之有效的分类器。     

采用混合核支持向量机的DOA估计

人工神经网络( ANN)进行建模时通常需要准备大量的数据样本,同时网络结构一般都比较复杂;而采用支持向量机( SVM)进行建模时,不同核函数有不同的效果,各有利弊,且选取SVM模型参数的理论支撑尚不完整。为了解决这些问题,提出了一种基于混合核函数的支持向量机来改善来波到达角( DOA)的估计性能,并结合二进制粒子群算法( PSO)来对混合核函数进行参数寻优。该混合核函数由全局核函数和局部核函数构成,提高了SVM的泛化能力和学习能力。首先通过拟合多项式函数,验证了该混合核SVM的有效性。将该方法用于DOA估计建模,在不同信噪比和快拍数下,通过与径向基函数( RBF)神经网络、基于各单一核函数的SVM和MUSIC算法预测结果对比,混合核SVM均方差有所降低,提高了DOA估计的精度且有更好的稳定性。     

Light field imaging based on a parallel SVM method for recognizing 2D fake pedestrians

It is novel to apply three-dimensional (3D) light field imaging technology to recognize two-dimensional (2D) fake pedestrians. In this paper, we propose a parallel support vector machine (SVM) method based on 3D light field imaging (light field camera) and machine learning techniques. A light field (LF) camera with robust sensors, which is able to record rich 3D information, is used as hardware equipment. Histogram of oriented gradient (HOG) feature extraction algorithm and SVM classification method are used to recognize the real and 2D fake pedestrians efficiently. Besides, we carry out an experiment on our improved LF pedestrian dataset. The experimental results of parameter optimization study show that in the case of 400 training samples (200 positive samples and 200 negative samples), 120 to 420 testing samples, and an HOG cellsize as 8×8, the best recognition accuracy with polynomial kernel function is improved by more than 2% compared with the previous method. The best accuracy is 99.17%. Otherwise, the recognition accuracy of more than 98.00% will be obtained even under other experimental conditions.     

基于局部空间变稀疏约束的多核学习方法

局部多核学习方法根据样本所在局部空间特性选择特定的核函数组合方式,具有较好的判别能力.本文提出了一种基于局部空间变稀疏约束的多核学习方法,首先依据样本在特征空间的分布情况以软分组的方式将训练数据划分为若干数据子集.以数据子集为单位,根据在相应的局部空间内的核函数相似程度,调整核组合的稀疏程度,使用交替优化的方法进行求解.实验表明本文方法对于区分特征学习和对抗噪声方面具有的优势,因此也使得在图像场景分类问题上的准确率和稳定性得到明显提高.     

基于人工蜂群算法的支持向量机参数优化及应用

为了解决常用的支持向量机(SVM)参数优化方法在寻优过程不同程度的陷入局部最优解的问题,提出一种基于人工蜂群(ABC)算法的SVM参数优化方法。将SVM的惩罚因子和核函数参数作为食物源位置,分类正确率作为适应度,利用ABC算法寻找适应度最高的食物源位置。利用4个标准数据集,将其与遗传(GA)算法、蚁群(ACO)算法、标准粒子群(PSO)算法优化的SVM进行性能比较,结果表明,本文方法能克服局部最优解,获得更高的分类正确率,并在小数目分类问题上有效降低运行时间。将本文方法运用到计算机笔迹鉴别,对提取的笔迹特征进行分类,与GA算法、ACO算法、PSO算法优化的SVM相比,得到了更高的分类正确率。     

DDoS attack detection and defense based on hybrid deep learning model in SDN

Software defined network (SDN) is a new kind of network technology,and the security problems are the hot topics in SDN field,such as SDN control channel security,forged service deployment and external distributed denial of service (DDoS) attacks.Aiming at DDoS attack problem of security in SDN,a DDoS attack detection method called DCNN-DSAE based on deep learning hybrid model in SDN was proposed.In this method,when a deep learning model was constructed,the input feature included 21 different types of fields extracted from the data plane and 5 extra self-designed features of distinguishing flow types.The experimental results show that the method has high accuracy,it’s better than the traditional support vector machine (SVM) and deep neural network (DNN) and other machine learning methods.At the same time,the proposed method can also shorten the processing time of classification detection.The detection model is deployed in SDN controller,and the new security policy is sent to the OpenFlow switch to achieve the defense against specific DDoS attack.     

基于改进遗传算法的入侵检测技术的设计与实现

天地一体化网络处在开放的电磁环境中,会时常遭受恶意网络入侵。为解决网络中绕过安全机制的非授权行为对系统进行攻击的问题,提出一种改进的遗传算法。该算法以决策树算法为适应度函数,通过删除数据集中的冗余特征,显著提高了对网络攻击的拦截率。通过机器学习进行异常分类,并利用遗传算法的特征选择功能,增强机器学习方法的分类效率。为验证算法的有效性,选用UNSW_NB15和UGRansome1819数据集进行训练和检测。使用随机森林、人工神经网络、K近邻和支持向量机等4种机器学习分类器进行评估,采用准确性、F1分数、召回率和混淆矩阵等指标评估算法的性能。实验证明,遗传算法作为特征选择工具能够显著提高分类准确性,并在算法性能上取得显著改善。同时,为解决弱分类器的不稳定性,提出一种集成学习优化技术,将弱分类器和强分类器集成进行优化。实验证实了该优化算法在提高弱分类器稳定性方面性能卓越。     

Low false positive learning with support vector machines

Most machine learning systems for binary classification are trained using algorithms that maximize the accuracy and assume that false positives and false negatives are equally bad. However, in many applications, these two types of errors may have very different costs. In this paper, we consider the problem of controlling the false positive rate on SVMs, since its traditional formulation does not offer such assurance. To solve this problem, we define a feature space sensitive area, where the probability of having false positives is higher, and use a second classifier (unanimity k-NN) in this area to better filter errors and improve the decision-making process. We call this method Risk Area SVM (RA-SVM). We compare the RA-SVM to other state-of-the-art methods for low false positive classification using 33 standard datasets in the literature. The solution we propose shows better performance in the vast majority of the cases using the standard Neyman–Pearson measure.     

Recognition of Electromyographic Signals Using Cascaded Kernel Learning Machine

Electromyographic (EMG) signals recognition is a complex pattern recognition problem due to its property of large variations in signals and features. This paper proposes a novel EMG classifier called cascaded kernel learning machine (CKLM) to achieve the goal of high-accuracy EMG recognition. First, the EMG signals are acquired by three surface electrodes placed on three different muscles. Second, EMG features are extracted by autoregressive model (ARM) and EMG histogram. After the feature extraction, the CKLM is performed to classify the features. CKLM is composed of two different kinds of kernel learning machines: generalized discriminant analysis (GDA) algorithm and support vector machine (SVM). By using GDA, both the goals of the dimensionality reduction of input features and the selection of discriminating features, named kernel FisherEMG, can be reached. Then, SVM combined with one-against-one strategy is executed to classify the kernel FisherEMG. By cascading SVM with GDA, the input features will be nonlinearly mapped twice by radial-basis function (RBF). As a result, a linear optimal separating hyperplane can be found with the largest margin of separation between each pair of postures' classes in the implicit dot product feature space. In addition, we develop a digital signal processor (DSP)-based EMG classification system for the control of a multi-degrees-of-freedom prosthetic hand for the practical implementation. Based on the clinical experiments, the results show that the proposed CKLM is superior to other frequently used methods, such as k-nearest neighbor algorithm, multilayer neural network, and SVM. The best EMG recognition rate 93.54% is obtained by CKLM.     

Efficient kernel functions for support vector machine regression model for analog circuits’ performance evaluation

Support vector machines (SVMs) have been widely used for creating fast and efficient performance macro-models for quickly predicting the performance parameters of analog circuits. These models have proved to be not only effective and fast but accurate also while predicting the performance. A kernel function is an integral part of SVM to obtain an optimized and accurate model. There is no formal way to decide, which kernel function is suited to a class of regression problem. While most commonly used kernels are radial basis function, polynomial, spline, multilayer perceptron; we have explored many other un-conventional kernel functions and report their efficacy and computational efficiency in this paper. These kernel functions are used with SVM regression models and these macromodels are tested on different analog circuits to check for their robustness and performance. We have used HSPICE for generating the set of learning data. Least Square SVM toolbox along with MATLAB was used for regression. The models which contained modified compositions of kernels were found to be more accurate and thus have lower root mean square error than those containing standard kernels. We have used different CMOS circuits varying in size and complexity as test vehicles—two-stage op amp, cascode op amp, comparator, differential op amp and voltage controlled oscillator.