Image superresolution using support vector regression.

A thorough investigation of the application of support vector regression (SVR) to the superresolution problem is conducted through various frameworks. Prior to the study, the SVR problem is enhanced by finding the optimal kernel. This is done by formulating the kernel learning problem in SVR form as a convex optimization problem, specifically a semi-definite programming (SDP) problem. An additional constraint is added to reduce the SDP to a quadratically constrained quadratic programming (QCQP) problem. After this optimization, investigation of the relevancy of SVR to superresolution proceeds with the possibility of using a single and general support vector regression for all image content, and the results are impressive for small training sets. This idea is improved upon by observing structural properties in the discrete cosine transform (DCT) domain to aid in learning the regression. Further improvement involves a combination of classification and SVR-based techniques, extending works in resolution synthesis. This method, termed kernel resolution synthesis, uses specific regressors for isolated image content to describe the domain through a partitioned look of the vector space, thereby yielding good results.     

Support Vector Regression Model for Measuring the Permittivity of Asphalt Concrete

The support vector regression (SVR) model for waveguide method of measuring the permittivity of asphalt concrete is presented in this letter. To validate the SVR model, simulated and measured data are employed. The training and testing data for the simulated SVR model are obtained by the reflection coefficient expression. While the testing data for the measured SVR model is obtained by HFSS. Experimental results suggest that the SVR model has a better performance in predicting the permittivity among microwave band. This SVR model could be applied to microwave industry as a kind of permittivity measurement tool.     

支撑向量机回归的简化SMO算法

统计学习理论中提出的支撑向量机回归(SVR)遵循了结构风险最小化原则,从而避免了一味追求经验风险最小化带来的弊端。采用扩展方法使SVR与支撑向量机分类(SVC)具有相似的数学形式,并在此基础上提出了一种用于SVR的简化SMO算法。与SVR现有的SMO算法相比,简化算法的数学形式简洁直观,在不增加算法空间和时间复杂度的前提下避免了大量繁复的判别条件,较大幅度地简化了算法实现,有利于SVR的广泛使用。     

基于残差预测修正的局部在线时间序列预测方法

 对于复杂的非线性和非平稳时间序列预测,基本的支持向量回归(Support Vecotr Regression,SVR)在线算法无法有效兼顾执行效率和预测精度.本文首先采用局部SVR进行时间序列建模预测,同步计算在线更新序列数据预测的残差,并采用Online SVR对残差序列进行混沌时间序列预测,将预测残差值实时补偿到局部SVR模型预测输出.实验结果表明,新方法在执行效率和预测精度方面较单一Online SVR均显著提高.     

Soft sensing of magnetic bearing system based on support vector regression and extended Kalman filter

The rotor displacement measurement plays an important role in an active bearing system, however, in practice this measurement might be quite noisy, so that the control performance might be seriously degraded. In this paper, a soft sensing method for magnetic bearing-rotor system based on Support Vector Regression (SVR) and Extended Kalman Filter (EKF) is proposed. In the proposed method, SVR technique is applied to model the acceleration of the rotor, which is regarded as a nonlinear function of rotor displacement, rotor velocity and bearing currents; then this SVR model is used to construct an EKF estimator of rotor displacement. In the proposed method the bearing current is incorporated to the estimation of displacement, so that displacement can be precisely estimated even if very large observation noise is present. A series of experiments are performed and the results verify the validity of the proposed displacement soft sensing method.     

基于组合SVR的非平稳时间序列的模糊建模方法

本文介绍一种对非平稳时间序列建模的新方法.参考Janos Abonyi提出的应用于时间序列的模糊分块算法,将该算法与改进的支持向量回归模型结合起来.首先,提出一种改进的支持向量回归的表达形式;然后,通过启发式的加权方法将模糊分块的信息与SVR结合起来;最后,提出一种基于组合SVR的建模方法.实验结果表明,本文提出的方法对于非平稳时间序列的建模具有较高的实用价值.     

Dosage individualization of erythropoietin using a profile-dependent support vector regression

The external administration of recombinant human erythropoietin is the chosen treatment for those patients with secondary anemia due to chronic renal failure in periodic hemodialysis. The objective of this paper is to carry out an individualized prediction of the EPO dosage to be administered to those patients. The high cost of this medication, its side-effects and the phenomenon of potential resistance which some individuals suffer all justify the need for a model which is capable of optimizing dosage individualization. A group of 110 patients and several patient factors were used to develop the models. The support vector regressor (SVR) is benchmarked with the classical multilayer perceptron (MLP) and the Autoregressive Conditional Heteroskedasticity (ARCH) model. We introduce a priori knowledge by relaxing or tightening the epsilon-insensitive region and the penalization parameter depending on the time period of the patients' follow-up. The so-called profile-dependent SVR (PD-SVR) improves results of the standard SVR method and the MLP. We perform sensitivity analysis on the MLP and inspect the distribution of the support vectors in the input and feature spaces in order to gain knowledge about the problem.     

Support Vector Regression Model for Millimeter Wave Transitions

In this paper, we introduce a new method, support vector regression (SVR) method, to model millimeter wave transitions. SVR is based on the structural risk minimization (SRM) principle, which leads to good generalization ability for regression problem. The SVR model can be electromagnetically developed with a set of training data and testing data which produced by the electromagnetic simulation. Two Ka-band millimeter wave transitions, i.e., waveguide to microstrip transition and coaxial to waveguide adapter, are used as examples to validate the method. Experimental results show that the developed SVR models have a good predictive ability, and they are useful for interactive CAD of millimeter wave transitions.     

用机器学习方法进行电力负荷宏观预测

分析了电力负荷宏观预测的模型和相关技术，引入支持向量回归方法(SVR)解决问题，并通过计算实例，比较分析了SVR与神经网络方法用于预测的效果，提出SVR广阔应用前景。     

MRI of moving subjects using multislice snapshot images with volume reconstruction (SVR): application to fetal, neonatal, and adult brain studies

Motion degrades magnetic resonance (MR) images and prevents acquisition of self-consistent and high-quality volume images. A novel methodology, Snapshot magnetic resonance imaging (MRI) with Volume Reconstruction (SVR) has been developed for imaging moving subjects at high resolution and high signal-to-noise ratio (SNR). The method combines registered 2-D slices from sequential dynamic single-shot scans. The SVR approach requires that the anatomy in question is not changing shape or size and is moving at a rate that allows snapshot images to be acquired. After imaging the target volume repeatedly to guarantee sufficient sampling every where, a robust slice-to-volume registration method has been implemented that achieves alignment of each slice within 0.3 mm in the examples tested. Multilevel scattered interpolation has been used to obtain high-fidelity reconstruction with root-mean-square (rms) error that is less than the noise level in the images. The SVR method has been performed successfully for brain studies on subjects that cannot stay still, and in some cases were moving substantially during scanning. For example, awake neonates, deliberately moved adults and, especially, on fetuses, for which no conventional high-resolution 3-D method is currently available. Fine structure of the in-utero fetal brain is clearly revealed for the first time and substantial SNR improvement is realized by having many individually acquired slices contribute to each voxel in the reconstructed image.     

Prediction of biomechanical properties of trabecular bone in MR images with geometric features and support vector regression

Whole knee joint MR image datasets were used to compare the performance of geometric trabecular bone features and advanced machine learning techniques in predicting biomechanical strength properties measured on the corresponding ex vivo specimens. Changes of trabecular bone structure throughout the proximal tibia are indicative of several musculoskeletal disorders involving changes in the bone quality and the surrounding soft tissue. Recent studies have shown that MR imaging also allows non-invasive 3-D characterization of bone microstructure. Sophisticated features like the scaling index method (SIM) can estimate local structural and geometric properties of the trabecular bone and may improve the ability of MR imaging to determine local bone quality in vivo. A set of 67 bone cubes was extracted from knee specimens and their biomechanical strength estimated by the yield stress (YS) [in MPa] was determined through mechanical testing. The regional apparent bone volume fraction (BVF) and SIM derived features were calculated for each bone cube. A linear multiregression analysis (MultiReg) and a optimized support vector regression (SVR) algorithm were used to predict the YS from the image features. The prediction accuracy was measured by the root mean square error (RMSE) for each image feature on independent test sets. The best prediction result with the lowest prediction error of RMSE = 1.021 MPa was obtained with a combination of BVF and SIM features and by using SVR. The prediction accuracy with only SIM features and SVR (RMSE = 1.023 MPa) was still significantly better than BVF alone and MultiReg (RMSE = 1.073 MPa). The current study demonstrates that the combination of sophisticated bone structure features and supervised learning techniques can improve MR-based determination of trabecular bone quality.     

Support vector regression via MCMC within evidence framework

This paper proposes a novel approach, Markov Chain Monte Carlo (MCMC) sampling approximation, to deal with intractable high-dimension integral in the evidence framework applied to Support Vector Regression (SVR). Unlike traditional variational or mean field method, the proposed approach follows the idea of MCMC, firstly draws some samples from the posterior distribution on SVR??s weight vector, and then approximates the expected output integrals by finite sums. Experimental results show the proposed approach is feasible and robust to noise. It also shows the performance of proposed approach and Relevance Vector Machine (RVM) is comparable under the noise circumstances. They give better robustness compared to standard SVR.     

An adaptive network traffic prediction approach for LDoS attacks detection

Low‐rate denial of service (LDoS) attacks reduce throughput and degrade quality of service (QoS) of network services by sending out attack packets with relatively low average rate. LDoS attack flows are difficult to detect from normal traffic since it has the property of low average rate. The research on network traffic analysis and modeling shows that network traffic measurement data are irregular nonlinear time series. To characterize and analyze network traffic between attack and non‐attack situations, the adaptive normal and abnormal ν‐support vector regression (ν‐SVR) prediction models are constructed on the basis of the reconstructed phase space. In this paper, the dimension of reconstructed phase space for ν‐SVR is optimized by Bayesian information criteria method, and the parameter in the radial basis function is adaptively adjusted by minimizing the within‐class distance and maximizing the between‐class distance in the feature space. The nonthreshold decision function is obtained through calculating the prediction error of adaptive normal and abnormal ν‐SVR prediction models, which is adopted to detect LDoS attacks. Experiments in NS‐2 environment show that the adaptive ν‐SVR prediction model can effectively predict the network traffic measurement time series, and the probability distribution of time series generated by the adaptive ν‐SVR prediction model is quite similar to that of the network traffic measurement data. Experiments also clearly demonstrate the superiority of the proposed approach in LDoS attacks detection.     

Fetal Weight Estimation Using the Evolutionary Fuzzy Support Vector Regression for Low-Birth-Weight Fetuses

Accurate estimation of fetal weight before delivery is of great benefit to limit the potential complication associated with the low-birth-weight infants. Although the regression analysis has been used as a daily clinical means to estimate the fetal weight on the basis of ultrasound measurements, it still lacks enough accuracy for low-birth-weight fetuses. The ineffectiveness is mainly due to the large inter- or intraobserver variability in measurements and the inappropriateness of the regression analysis. A novel method based on the support vector regression (SVR) is proposed to improve the weight estimation accuracy for fetuses of less than 2500 g. Here, fuzzy logic is introduced into SVR (termed FSVR) to limit the contribution of inaccurate training data to the model establishment, and thus, to enhance the robustness of FSVR to noisy data. To guarantee the generalization performance of the FSVR model, the nondominated sorting genetic algorithm (NSGA) is utilized to obtain the optimal parameters for the FSVR, which is referred to as the evolutionary fuzzy support vector regression (EFSVR) model. Compared with regression formulas, back-propagation neural network, and SVR, EFSVR achieves the lowest mean absolute percent error (6.6%) and the highest correlation coefficient (0.902) between the estimated fetal weight and the actual birth weight. The EFSVR model produces significant improvement (1.9%-4.2%) on the accuracy of fetal weight estimation over several widely used formulas. Experiments show the potential of EFSVR in clinical prenatal care.     

Support vector regression-based study of interference in absorption spectral lines of mixed gases

When measuring the concentration of multi-component gas mixtures based on supercontinuum laser absorption spectroscopy (SCLAS), there are interferences between the absorption spectral lines. For the spectral interference problem of CO2 and CH4 at 1 432 nm, a method based on support vector regression (SVR) is proposed in this paper. The SVR model, the k-nearest neighbor (KNN) model and the least squares (LS) model are used to analyze and predict the absorption spectral data, and the prediction accuracies were 96.29%, 88.89% and 85.19%, respectively, with the highest prediction accuracy of the SVR model. The results show that the method can accurately measure the concentration of gas mixtures, realize the detection of mixed gases using a single waveband, and provide a solution to the overlapping spectral line interference of multi-component gas mixtures.     

一种求解支持向量回归波束形成的算法

在对传统求解支持向量回归算法研究与分析的基础上,针对支持向量回归模型,结合支持向量回归的波束形成技术,提出了一种利用迭代重加权最小二乘支持向量回归波束形成的算法,并对具有严重干扰的接收信号进行了数值仿真试验和对比分析。结果表明：基于迭代重加权最小二乘支持向量回归波束形成的算法不同于传统的标准二次型算法,收敛速度快,干扰抑制强,计算量小,降低了计算复杂度,避免了二次规划技术的高计算成本,提高了算法效率,并保持了良好的泛化能力,具有一定的参考价值。     

基于小波变换与支持向量机回归的冬小麦叶面积指数估算

叶面积指数(LAI)是作物长势诊断及产量预测的重要参数。通过对冬小麦采样点的高光谱曲线进行连续小波变换(CWT),然后利用小波系数与LAI 建立支持向量机回归(SVR)模型,实现冬小麦不同生育时期的叶面积指数估算。通过对所研究方法与选取的植被指数、偏最小二乘(PLS)回归等5种方法的反演结果进行统计分析。结果表明:利用连续小波变换确定的LAI 的敏感波段为680、739、802、895 nm,对应尺度分别为8、4、9 和8,对应小波系数的LAI 回归确定系数(R2)明显高于冠层反射率的回归确定系数;利用小波系数与LAI 建立的SVR 模型的反演精度最高,模型实测值与预测值的检验精度(R2)为0.86,均方根误差(RMSE)为0.43;而常用植被指数(归一化植被指数,NDVI;比值植被指数,RVI)建立的估测模型对冬小麦多个生育时期LAI 反演精度最低(R2 0.76,RMSE0.56)。因此利用连续小波变换进行数据预处理,能更好地筛选出对叶面积指数敏感的信息,LAI 回归方法比较结果表明,SVR 比PLS 更适合于LAI 的估测,通过将CWT 与SVR 结合(CWT-SVR)能实现不同生育时期冬小麦叶面积指数的遥感估算。     

Use of Support Vector Regression in Stable Trajectory Generation for Walking Humanoid Robots

This paper concerns the use of support vector regression (SVR), which is based on the kernel method for learning from examples, in identification of walking robots. To handle complex dynamics in humanoid robot and realize stable walking, this paper develops and implements two types of reference natural motions for a humanoid, namely, walking trajectories on a flat floor and on an ascending slope. Next, SVR is applied to model stable walking motions by considering these actual motions. Three kinds of kernels, namely, linear, polynomial, and radial basis function (RBF), are considered, and the results from these kernels are compared and evaluated. The results show that the SVR approach works well, and SVR with the RBF kernel function provides the best performance. Plus, it can be effectively applied to model and control a practical biped walking robot.     

边缘网络中一种VNF需求预测方法

针对当前虚拟网络功能（Virtualization Network Functions,VNF）需求预测方法准确率较低且不适用于边缘网络的问题,提出了一种在边缘网络中基于支持向量回归（Support Vector Regression,SVR）与门控循环单元（Gated Recurrent Unit,GRU）神经网络模型结合的VNF需求预测方法。考虑到网络边缘流量具有突发性、自相似性及长相关性等特点,结合SVR和GRU两种模型的优点,利用计算复杂度较低的SVR和GRU模型分别提取网络服务历史时序数据的短期特征和长期特征,以提高VNF需求预测准确率,实现边缘网络中VNF的提前放置。实验表明,所提出的预测方法在边缘网络中针对不同网络服务的预测较于传统方法、循环神经网络（Recurrent Neural Networks,RNN）、长短期记忆网络（Long Short-Term Memory,LSTM）模型能够降低20%~30%的误差,有更佳的预测效果。     

基于SVR的语音变换映射规则研究

该文介绍了语音变换与支持向量回归(SVR)的基本理论。提出了基于多输出支持向量回归的语音变换特征参数映射规则,并对该映射规则进行了仿真实验。对变换后语音所进行的主客观测试表明,该映射规则对比码书映射和高斯混合模型,能够在参数映射离散性和平滑性之间有效折中,提高语音可懂度。