基于支持向量回归的唇动参数预测

支持向量机学习方法以结构风险最小化原则取代传统机器学习方法中的经验风险最小化原则，在有限样本的机器学习中显示出优异的性能．将这一新的统计学习方法应用到多媒体交互作用的研究中，用支持向量回归的方法由语音预测唇动参数．通过对语音的线性预测系数进行主分量分析，有效地压缩了声学特征参数的维数．结合交叉校验和最速下降优化方法，选择最佳的支持向量回归学习参数．在汉语0～9的任意数字串上对唇高参数的预测实验结果达到了均方误差0．0096，平均幅度误差7．2％及相关系数0．8的效果．这一结果优于一个文中优化过的人工神经网络所达到的性能，说明这一方法很有潜力．     

基于支持向量回归机的公路货运量预测模型*

为了提高公路货运量预测的能力,应用基于结构风险最小化准则的标准支持向量回归机方法来研究公路货运量预测问题.在选择适当的参数和核函数的基础上,通过对成都公路货运量时间序列进行预测,并与人工神经网络、线性回归分析等方法进行了对比,发现该方法能获得最小的训练相对误差和测试相对误差.     

Time Series Prediction Using Support Vector Machines: A Survey

Time series prediction techniques have been used in many real-world applications such as financial market prediction, electric utility load forecasting , weather and environmental state prediction, and reliability forecasting. The underlying system models and time series data generating processes are generally complex for these applications and the models for these systems are usually not known a priori. Accurate and unbiased estimation of the time series data produced by these systems cannot always be achieved using well known linear techniques, and thus the estimation process requires more advanced time series prediction algorithms. This paper provides a survey of time series prediction applications using a novel machine learning approach: support vector machines (SVM). The underlying motivation for using SVMs is the ability of this methodology to accurately forecast time series data when the underlying system processes are typically nonlinear, non-stationary and not defined a-priori. SVMs have also been proven to outperform other non-linear techniques including neural-network based non-linear prediction techniques such as multi-layer perceptrons.The ultimate goal is to provide the reader with insight into the applications using SVM for time series prediction, to give a brief tutorial on SVMs for time series prediction, to outline some of the advantages and challenges in using SVMs for time series prediction, and to provide a source for the reader to locate books, technical journals, and other online SVM research resources.     

Support vector machines models for surface roughness prediction in CNC turning of AISI 304 austenitic stainless steel

In the present investigation, three different type of support vector machines (SVMs) tools such as least square SVM (LS-SVM), Spider SVM and SVM-KM and an artificial neural network (ANN) model were developed to estimate the surface roughness values of AISI 304 austenitic stainless steel in CNC turning operation. In the development of predictive models, turning parameters of cutting speed, feed rate and depth of cut were considered as model variables. For this purpose, a three-level full factorial design of experiments (DOE) method was used to collect surface roughness values. A feedforward neural network based on backpropagation algorithm was a multilayered architecture made up of 15 hidden neurons placed between input and output layers. The prediction results showed that the all used SVMs results were better than ANN with high correlations between the prediction and experimentally measured values.     

Fast minimization of structural risk by nearest neighbor rule

In this paper, we present a novel nearest neighbor rule-based implementation of the structural risk minimization principle to address a generic classification problem. We propose a fast reference set thinning algorithm on the training data set similar to a support vector machine (SVM) approach. We then show that the nearest neighbor rule based on the reduced set implements the structural risk minimization principle, in a manner which does not involve selection of a convenient feature space. Simulation results on real data indicate that this method significantly reduces the computational cost of the conventional SVMs, and achieves a nearly comparable test error performance.     

Predicting river daily flow using wavelet-artificial neural networks based on regression analyses in comparison with artificial neural networks and support vector machine models

This study investigates the ability of wavelet-artificial neural networks (WANN) for the prediction of short-term daily river flow. The WANN model is improved by conjunction of two methods, discrete wavelet transform and artificial neural networks (ANN) based on regression analyses, respectively. The proposed WANN models are applied to the daily flow data of Vanyar station, on the Ajichai River in the northwest region of Iran, and compared with the ANN and support vector machine (SVM) techniques. Mean square error (MSE), mean absolute error (MAE) and correlation coefficient (R) statistics are used for evaluating precision of the WANN, ANN and SVM models. Comparison results demonstrate that the WANN model performs better than the ANN and SVM models in short-term (1-, 2- and 3-day ahead) daily river flow prediction.

     

OAO-SVMs的训练时间性能分析及算法改进

支持向量机(SVM)算法是统计学习理论中最年轻的分支。结构风险最小化原则使其具有良好的学习推广性。但在实际应用中,训练速度慢一直是支持向量机理论几个亟待解决的问题之一,这一点在SVM向多类问题领域推广时表现的尤为明显。文中将从样本分布与类别数量两方面入手,对传统的SVM多分类OAO算法进行训练时间性能上的分析,并引入分层的思想,提出传统OAO-SVMs算法的改进模型H-OAO-SVMs。通过与其他常见多分类SVMs训练时间的比较表明:改进后的H-OAO-SVMs模型具有更优的训练时间性能。     

Demand forecasting of perishable farm products using support vector machine

This article presents a new algorithm for forecasting demand for perishable farm products, based on the support vector machine (SVM) method. Since SVMs have greater generalisation performance and guarantee global minima for given training data, it is believed that support vector regression will perform well for forecasting demand for perishable farm products. In order to improve forecasting precision (FP), this article quantifies the factors affecting the sales forecast of perishable farm products based on the fuzzy theory, which is suitable for real situations. Numerical experiments show that forecasting systems with SVMs and fuzzy theory outperform the radial basis function neural network, based on the criteria of day absolute error, relative mean error and FP. Since there is no structured way to choose the free parameters of SVMs, the variational range of free parameters and the effects of the parameters on prediction performance are discussed in this article. Analysis of experimental results proves that it is advantageous to apply SVMs forecasting system in perishable farm products demand forecasting.     

Online option price forecasting by using unscented Kalman filters and support vector machines

This study develops a hybrid model that combines unscented Kalman filters (UKFs) and support vector machines (SVMs) to implement an online option price predictor. In the hybrid model, the UKF is used to infer latent variables and make a prediction based on the Black–Scholes formula, while the SVM is employed to model the nonlinear residuals between the actual option prices and the UKF predictions. Taking option data traded in Taiwan Futures Exchange, this study examined the forecasting accuracy of the proposed model, and found that the new hybrid model is superior to pure SVM models or hybrid neural network models in terms of three types of options. This model can help investors for reducing their risk in online trading.     

Effective recognition of control chart patterns in autocorrelated data using a support vector machine based approach

The effective recognition of unnatural control chart patterns (CCPs) is a critical issue in statistical process control, as unnatural CCPs can be associated with specific assignable causes adversely affecting the process. Machine learning techniques, such as artificial neural networks (ANNs), have been widely used in the research field of CCP recognition. However, ANN approaches can easily overfit the training data, producing models that can suffer from the difficulty of generalization. This causes a pattern misclassification problem when the training examples contain a high level of background noise (common cause variation). Support vector machines (SVMs) embody the structural risk minimization, which has been shown to be superior to the traditional empirical risk minimization principle employed by ANNs. This research presents a SVM-based CCP recognition model for the on-line real-time recognition of seven typical types of unnatural CCP, assuming that the process observations are AR(1) correlated over time. Empirical comparisons indicate that the proposed SVM-based model achieves better performance in both recognition accuracy and recognition speed than the model based on a learning vector quantization network. Furthermore, the proposed model is more robust toward background noise in the process data than the model based on a back propagation network. These results show the great potential of SVM methods for on-line CCP recognition.     

区间时间序列的混合预测模型

提出一种基于自回归求和移动平均(ARIMA) 与人工神经网络(ANN) 的区间时间序列混合模型, 并用混合模型分别对区间中值序列和区间半径序列建模. 采用Monte Carlo 方法生成模拟区间序列, 分别用ARIMA、ANN和混合模型3 种方法进行建模和预测实验, 并用统计学方法检验模型误差. 最后分别采用3 种方法对H市轨道交通某号线牵引能耗区间序列进行了建模和预测, 实验结果表明混合模型的建模精度和预测性能均优于单一模型.

     

Short-term fault prediction based on support vector machines with parameter optimization by evolution strategy

Support vector machines (SVMs) are the effective machine-learning methods based on the structural risk minimization (SRM) principle, which is an approach to minimize the upper bound risk functional related to the generalization performance. The parameter selection is an important factor that impacts the performance of SVMs. Evolution Strategy with Covariance Matrix Adaptation (CMA-ES) is an evolutionary optimization strategy, which is used to optimize the parameters of SVMs in this paper. Compared with the traditional SVMs, the optimal SVMs using CMA-ES have more accuracy in predicting the Lorenz signal. The industry case illustrates that the proposed method is very successfully in forecasting the short-term fault of large machinery.     

High performance concrete compressive strength forecasting using ensemble models based on discrete wavelet transform

This paper investigates the use of wavelet ensemble models for high performance concrete (HPC) compressive strength forecasting. More specifically, we incorporate bagging and gradient boosting methods in building artificial neural networks (ANN) ensembles (bagged artificial neural networks (BANN) and gradient boosted artificial neural networks (GBANN)), first. Coefficient of determination (R²), mean absolute error (MAE) and the root mean squared error (RMSE) statics are used for performance evaluation of proposed predictive models. Empirical results show that ensemble models (R²_BANN=0.9278, R²_GBANN=0.9270) are superior to a conventional ANN model (R²_ANN=0.9088). Then, we use the coupling of discrete wavelet transform (DWT) and ANN ensembles for enhancing the prediction accuracy. The study concludes that DWT is an effective tool for increasing the accuracy of the ANN ensembles (R²_WBANN=0.9397, R²_WGBANN=0.9528).     

基于支持向量机的脑-机接口模式分类和模型参数研究

脑—机接口（BCI）是连接大脑和计算机及外部设备的通讯系统,通过连续小波变换（CWT）对采集的脑电信号进行分解,构造由多个尺度对应的方差构成的多维向量,应用支持向量机（SVM）进行分类识别,取得了良好的效果。基于统计学习理论的结构化风险最小化原则,研究了高斯核支持向量机误差惩罚参数C和高斯核参数σ对支持向量机性能的影响,使用仿真实验验证了传统的经验风险最小化原则不能保证良好的推广能力,提出了综合调整参数σ和参数C的方法以优化支持向量机的性能。     

基于支持向量机的复杂背景下的人体检测

常用的人体检测方法多是基于经验风险最小化原理的传统统计理论，其性能只有在样本趋于无穷大时才有理论上的保证，而在实际应用中，学习样本通常是有限的。针对传统统计理论在人体检测中存在的不足，提出了一种基于统计学习理论——支持向量机(SVM)的人体检测方法，利用彩色空间对背景进行自适应建模提取运动目标，然后使用训练好的SVM进行验证是否是人体。为了简化SVM分类器的设计及提高机器学习的效率，提出了一种星形向量表示法用于抽取目标的特征向量，并且用实验方法得到了这种表示法的最优表示。将SVM与ANN进行比较，并且对不同内积函数的SVM的性能也进行了比较。实验结果表明，SVM的性能要优于ANN，并且采用径向基函数的SVM性能最好。该方法鲁棒性强，正确率高，解决了复杂背景下运动人体实时检测的一些关键问题。     

电力负荷短期预测的支持向量回归参数自动赋值研究

电力系统负荷预测是当前国内外的研究热点,支持向量回归算法是一种解决电力系统负荷预测问题非常有效的方法,如何根据特定数据集选择合适的模型参数,以保证建立好的模型有很好的推广性能,成为设计支持向量回归机的关键一步。本文采用了1-范数、2-范数以及v-支持向量回归算法来解决支持向量机参数的自动复制问题。在真实数据集上的实验结果表明,新模型在预测能力上较之一些广泛使用的软件可靠性预测模型有明显的提高。     

Forecasting reservoir monthly runoff via ensemble empirical mode decomposition and extreme learning machine optimized by an improved gravitational search algorithm

Monthly streamflow prediction plays a significant role in reservoir operation and water resource management. Hence, this research tries to develop a hybrid model for accurate monthly streamflow prediction, where the ensemble empirical mode decomposition (EEMD) is firstly used to decompose the original streamflow data into a finite amount of intrinsic mode functions (IMFs) and a residue; and then the extreme learning machine (ELM) is employed to forecast each IMFs and the residue, while an improved gravitational search algorithm (IGSA) based on elitist-guide evolution strategies, selection operator and mutation operator is used to select the parameters of all the ELM models; finally, the summarized predicated results for all the subcomponents are treated as the final forecasting result. The hybrid method is applied to forecast the monthly runoff of Three Gorges in China, while four quantitative indexes are used to test the performances of the developed forecasting models. The results show that EEMD can effectively separate the internal characteristics of the original monthly runoff, and the hybrid model is able to make an obvious improvement over other models in hydrological time series prediction.     

SVM和ANN在网络安全风险评估中的比较研究

支持向量机和人工神经网络是人工智能方法的两个分支,详细介绍了支持向量机和人工神经网络原理。建立了网络安全评估指标体系,将支持向量机和人工神经网络同时应用于网络安全风险评估的过程中,通过实例比较了两者的评估效果,结果表明了支持向量机在小样本情况下分类正确率普遍高于人工神经网络,具有较好的分类能力和泛化能力;同时在训练时间上也有绝对的优势。实践证实了支持向量机用于网络安全风险评估的有效性和优越性。     

Integrating nonlinear graph based dimensionality reduction schemes with SVMs for credit rating forecasting

By integrating graph based nonlinear dimensionality reduction with support vector machines (SVMs), this study develops a novel prediction model for credit ratings forecasting. SVMs have been successfully applied in numerous areas, and have demonstrated excellent performance. However, due to the high dimensionality and nonlinear distribution of the input data, this study employed a kernel graph embedding (KGE) scheme to reduce the dimensionality of input data, and enhance the performance of SVM classifiers. Empirical results indicated that one-vs-one SVM with KGE outperforms other multi-class SVMs and traditional classifiers. Compared with other dimensionality reduction methods the performance improvement owing to KGE is significant.     

Semiparametric ARX neural-network models with an application toforecasting inflation

We examine semiparametric nonlinear autoregressive models with exogenous variables (NLARX) via three classes of artificial neural networks: the first one uses smooth sigmoid activation functions; the second one uses radial basis activation functions; and the third one uses ridgelet activation functions. We provide root mean squared error convergence rates for these ANN estimators of the conditional mean and median functions with stationary β-mixing data. As an empirical application, we compare the forecasting performance of linear and semiparametric NLARX models of US inflation. We find that all of our semiparametric models outperform a benchmark linear model based on various forecast performance measures. In addition, a semiparametric ridgelet NLARX model which includes various lags of historical inflation and the GDP gap is best in terms of both forecast mean squared error and forecast mean absolute deviation error