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One of the important types of lightweight concrete is expanded polystyrene (EPS) concrete which can be constructed by substituting part of coarse aggregates of the concrete with EPS beads. The characteristics of EPS concrete is greatly dependent on the material which is used in its manufacturing. Present study aims to develop two soft computing approaches according to radial basis function neural network (RBFNN) and coupled simulated annealing-least square support vector machine (CSA-LSSVM) models and a regression model for approximation the compressive strength of various types of concrete with main focus of EPS concrete. The regression model was considered as the benchmark model and the outcomes of RBFNN and CSA-LSSVM models were compared with it. The results revealed that the developed CSA-LSSVM and RBFNN approaches can be efficiently utilized to estimate the compressive strength of various kinds of concrete. The outcomes of the regression model were not as accurate as those of the machine learning approaches. In addition, the CSA-LSSVM model developed by the use of radial basis kernel function was decided as a better model. The R2, AARD%, RMSE, and MAPE for the benchmark model were 0.7555, 8.87%, 25.49, and 0.00887; however, these values for CSA-LSSVM were obtained as 0.9970, 3.01%, 1.08, and 0.0301 and for the RBFNN model were obtained to be 0.9953, 4.84%, 1.23, and 0.0484, respectively. [ABSTRACT FROM AUTHOR]

In this study, the performance of a counter-flow Ranque–Hilsch vortex tube (RHVT) was investigated experimentally using working fluid nitrogen, and the thermal performance was modeled using different modeling methods with these experimental results. Each method was compared with the others. The variation of ΔT, which is the measure of temperature separation in RHVT, was investigated by using nozzle number, the thermal conductivity of nozzle material, inlet pressure, specific heat, and density of working fluid. In the study, the prediction models of linear, k-nearest neighbor (kNN), random forest (RF), and support vector machine (SVM) regression were trained with measured thermal performance using a specific portion of the experimental data and tested with the remaining data. Two different train and test datasets were used with the ratio of experimental data as 90–10% and 80–20%, respectively. The highest accuracy ratio was determined at the end of the four methods with SVM regression as 96.01% when using the train and test datasets as 90–10%, respectively. The percent accuracy of the other models under the same conditions was calculated as 95.7, 90.87, and 78.36% for RF, kNN, and linear, respectively. [ABSTRACT FROM AUTHOR]

As a kind of time series, the export volume of foreign trade has the characteristics of randomness, complexity, strong nonlinearity and noise, so it is difficult to describe it by using the traditional time series model algorithm. Support vector neural network (SVNN) has many unique advantages in solving small sample, nonlinear and high-dimensional pattern recognition problems. This paper uses the method of support vector neural network to predict and analyze China's foreign trade export and uses principal component analysis and regression analysis to analyze the contribution rate of different influencing factors to foreign trade export. The results show that:(1) the contribution rate of domestic economic factors to China's foreign trade export is the largest, reaching 59.65%, which also reflects the necessity and correctness of China's insistence on supply-side reform. (2) The nonlinear prediction results of the support vector neural network have a good fitting with the actual value of China's foreign trade export, and the prediction error of the support vector neural network is controlled within 10%, showing a good prediction effect; (3) neural network method has good modeling and generalization ability for nonstationary small sample import and export time series data and can achieve high prediction accuracy and decision judgment accuracy, especially for the prediction of its development trend, and the model has a high degree of fitting. [ABSTRACT FROM AUTHOR]

With the rapid development of machine learning technology, as a regression problem that helps people to find the law from the massive data to achieve the prediction effect, more and more people pay attention. Data prediction has become an important part of people's daily life. Currently, the technology is widely used in many fields such as weather forecasting, medical diagnosis and financial forecasting. Therefore, the research of machine learning algorithms in regression problems is a research hotspot in the field of machine learning in recent years. However, real-world regression problems often have very complex internal and external factors, and various machine learning algorithms have different effects on scalability and predictive performance. In order to better study the application effect of machine learning algorithm in regression problem, this paper mainly adopts three common machine learning algorithms: BP neural network, extreme learning machine and support vector machine. Then, by comparing the effects of the single model and integrated model of these machine learning algorithms in the application of regression problems, the advantages and disadvantages of each machine learning algorithm are studied. Finally, the performance of each machine learning algorithm in regression prediction is verified by simulation experiments on four different data sets. The results show that the research on several machine learning algorithms and their integration models has certain feasibility and rationality. [ABSTRACT FROM AUTHOR]

It is key indexes of worsted yarn quality such as worsted yarn strength index, etc., and it can well control worsted yarn quality by predicting yarn strength index, etc. Generally, it is generally used to predict yarn strength indexes such as multiple linear regression (MLR) algorithm, support vector machine (SVM) and backpropagation neural network (BPNN). This paper proposes a new neural network; it combines convolutional neural network (CNN) with general regression neural network (GRNN), which is written as the CNN–GRNN. It used 1900 sets of data to train CNN–GRNN, SVM and BPNN. It tested CNN–GRNN, MLR, SVM and BPNN with 10 sets of data. The CNN–GRNN neural network is the best accuracy among these four algorithms. [ABSTRACT FROM AUTHOR]

Twin support vector regression (TSVR) aims at finding 휖-insensitive up- and down-bound functions for the training points by solving a pair of smaller-sized quadratic programming problems (QPPs) rather than a single large one as in the conventional SVR. So TSVR works faster than SVR in theory. However, TSVR gives equal emphasis to the points above the up-bound and below the down-bound, which leads to the same influences on the regression function. In fact, points in different positions have different effects on the regressor. Inspired by it, we propose an asymmetric ν-twin support vector regression based on pinball loss function (Asy- ν-TSVR). The new algorithm can effectively control the fitting error by tuning the parameters ν and p. Therefore, it enhances the generalization ability. Moreover, we study the distribution of samples and give the upper bounds for the samples locating in different positions. Numerical experiments on one artificial dataset, eleven benchmark datasets and a real wheat dataset demonstrate the validity of our proposed algorithm. [ABSTRACT FROM AUTHOR]

Support vector regression (SVR) and neural network (NN) models were used to predict average daily gain (ADG), feed efficiency (FE), and feed intake (FI) of broiler chickens during the starter period. Input variables for construction of the models were levels of dietary protein and branched-chain amino acids (BCAA; valine, isoleucine, and leucine). Starting with 241 lines, the SVR and NN models were trained using 120 data lines and the remainder (n = 121) was used as the testing set. The SVR models were developed using different kernel functions including: linear, polynomial (second and third order), radial basis function (RBF), and sigmoidal. In order to evaluate the SVR models, their performance was compared to that of multilayer perceptron (MLP)- and RBF-type NN models. Results indicated that MLP-type NN models were the most accurate in predicting the investigated output variables (R2 for ADG in training and testing = 0.81 and 0.81; FE = 0.87 and 0.87; FI = 0.68 and 0.62). Among the different SVR kernels, best performance was achieved with the RBF (R2 for ADG in training and testing = 0.76 and 0.76; FE = 0.85 and 0.87; FI = 0.46 and 0.48) and polynomial (third order) function (R2 for ADG in training and testing = 0.77 and 0.77; FE = 0.85 and 0.87; FI = 0.46 and 0.39), whose prediction ability was better than that of the RBF-type NN (R2 for ADG in training and testing = 0.75 and 0.75; FE = 0.82 and 0.82; FI = 0.41 and 0.39) models. Sigmoidal SVR models provided the poorest prediction. The work demonstrates that, through proper selection of kernel functions and corresponding parameters, SVR models can be considered as an alternative to NN models in predicting the response of broiler chickens to protein and BCAA. This type of model should also be applicable in poultry and other areas of animal nutrition. [ABSTRACT FROM AUTHOR]

Recently, extreme learning machine (ELM) has attracted increasing attention due to its successful applications in classification, regression, and ranking. Normally, the desired output of the learning system using these machine learning techniques is a simple scalar output. However, there are many applications in machine learning which require more complex output rather than a simple scalar one. Therefore, structured output is used for such applications where the system is trained to predict structured output instead of simple one. Previously, support vector machine (SVM) has been introduced for structured output learning in various applications. However, from machine learning point of view, ELM is known to offer better generalization performance compared to other learning techniques. In this study, we extend ELM to more generalized framework to handle complex outputs where simple outputs are considered as special cases of it. Besides the good generalization property of ELM, the resulting model will possesses rich internal structure that reflects task-specific relations and constraints. The experimental results show that structured ELM achieves similar (for binary problems) or better (for multi-class problems) generalization performance when compared to ELM. Moreover, as verified by the simulation results, structured ELM has comparable or better precision performance with structured SVM when tested for more complex output such as object localization problem on PASCAL VOC2006. Also, the investigation on parameter selections is presented and discussed for all problems. [ABSTRACT FROM AUTHOR]

In this paper, a simplification of the necessary and sufficient Karush-Kuhn-Tucker (KKT) optimality conditions for the Lagrangian support vector regression in 2-norm resulting a naïve root finding problem of a system of equations in m variables is proposed, where m is the number of training examples. However, since the resulting system of equations contains terms having the nonsmooth “plus” function, two approaches are considered: (i) the problem is reformulated into an equivalent absolute value equation problem and solved by functional iterative and generalized Newton methods and (ii) solve the original root finding problem by generalized Newton method. The proofs of convergence and pseudo-codes of the iterative methods are given. Numerical experiments performed on a number of synthetic and real-world benchmark datasets showing similar generalization performance with much faster learning speed in comparison with support vector regression (SVR) and as good as performance in comparison with the least squares SVR, unconstrained Lagrangian SVR proposed in Balasundaram et al. (Neural Netw 51:67-79, 2014), twin SVR and extreme learning machine clearly indicate the effectiveness and suitability of the proposed problem formulation solved by functional iterative and generalized Newton methods. [ABSTRACT FROM AUTHOR]

This work develops a hybridized support vector regression (HSVR)-based model for accurate estimation of melting points of fatty acids using their molecular weights and the number of carbon-carbon double bond as descriptors. The development of HSVR-based model is characterized with two stages. The first stage involves training and testing SVR using test-set-cross validation technique with molecular weights and the number of carbon-carbon double bond as descriptors, while the second stage utilizes the estimated melting points obtained from the first stage as descriptor for further training and testing of SVR. The proposed hybrid system therefore demonstrates a better predictive and generalization ability than ordinary SVR. Furthermore, the melting points of sixty-two fatty acids estimated using the proposed HSVR-based model show persistence closeness with the experimental values than the results of other existing predictive models for fatty acids melting points estimation such as Guijie et al. model and Guendouzi model. The developed HSVR-based model is also characterized with higher value of coefficient of correlation and lower value of mean absolute error than that of the existing predictive models. Superiority of the developed HSVR-based model over the existing predictive models in terms of the ease of obtaining its descriptors and the accuracy of its estimates is advantageous to unravel estimation challenges associated with determination of fatty acids melting points. [ABSTRACT FROM AUTHOR]

This paper introduces a novel generalized self-tuning regulator based on online support vector regression (OSVR) for nonlinear systems. The main idea is to approximate the parameters of an adaptive controller by optimizing the regression margin between reference input and system output. For this purpose, 'closed-loop margin' which depends on tracking error is defined, and the parameters of the adaptive controller are optimized so as to minimize the tracking error which leads simultaneously to the optimization of the closed-loop margin. The overall architecture consists of an online SVR which computes a forward model of the system, an adaptive controller with tunable parameters and an adaptation mechanism realized by separate online SVRs to estimate each tunable controller parameter. The proposed architecture is implemented with adaptive proportional-integral-derivative (PID) and adaptive fuzzy PID in the controller block. The performance of the generalized self-tuning regulator mechanism has been examined via simulations performed on a bioreactor benchmark system, and the results show that the generalized adaptive controller and OSVR model attain good control and modeling performances. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Prediction of time series data is of relevance for many industrial applications. The prediction can be made in one-step and multi-step ahead. For predictive maintenance, multi-step-ahead prediction is of interest for projecting the evolution of the future conditions of the equipment of interest, computing the remaining useful life and taking corresponding maintenance decisions. Recursive prediction is one of the popular strategies for multi-step-ahead prediction. SVM is a popular data-driven approach that has been used for recursive multi-step-ahead prediction. Tuning the hyperparameters in SVM during the training process is challenging, and normally the hyperparameters are tuned by solving an optimization problem. This paper analyses the possible objectives of the optimization for tuning hyperparameters. Through experiments on one synthetic dataset and two real time series data, related to the prediction of wind speed in a region and leakage from the reactor coolant pump in a nuclear power plant, a bi-objective optimization combining mean absolute derivatives and accuracy on all prediction steps is shown to be the best choice for tuning SVM hyperparameters for recursive multi-step-ahead prediction. [ABSTRACT FROM AUTHOR]

Support vector machines (SVMs) are an emerging and powerful technique in coping with classification problems. However, a lack of rule generation is a weakness of the SVM model, especially in analyzing sporting results. This investigation developed a hybrid model integrating the SVM technique and a decision tree approach (HSVMDT) to predict the results of basketball games, and to provide rules to aid coaches in developing strategies. The HSVMDT model employed the unique strength of SVM and decision tree in generating rules and predicting the outcomes of games. With predicted outcomes of games, and rules yielded from the HSVMDT model, coaches can easily and quickly learn essential factors increasing the chances to win games. Empirical results showed that the proposed HSVMDT model can obtain relatively satisfactory prediction accuracy and therefore is a promising alternative for analyzing the results of basketball competitions. [ABSTRACT FROM AUTHOR]

We extend LS-SVM to ordinal regression, which has wide applications in many domains such as social science and information retrieval where human-generated data play an important role. Most current methods based on SVM for ordinal regression suffer from the problem of ignoring the distribution information reflected by the samples clustered around the centers of each class. This problem would degrade the performance of SVM-based methods since the classifiers only depend on the scattered samples on the border which induce large margin. Our method takes the samples clustered around class centers into account and has a competitive computational complexity. Moreover, our method would easily produce the optimal cut-points according to the prior class probabilities and hence may obtain more reasonable results when the prior class probabilities are not the same. Experiments on simulated datasets and benchmark datasets, especially on the real ordinal datasets, demonstrate the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Upper body power (UBP) is a major parameter in cross-country skiing that strongly influences the performance of skiers during races. Although the direct measurement of UBP in laboratory environment provides the most accurate assessment, practical difficulties such as the need of sophisticated and expensive testing equipment and trained stuff have given rise to the necessity of predicting and identifying the relevant predictors of UBP. This study aims to develop feature selection-based prediction models to predict 10-s UBP (UBP) and 60-s UBP (UBP) of cross-country skiers with the help of support vector machines (SVMs) using different kernel functions. For model testing, tenfold cross-validation has been carried out, and the performance of the models has been assessed by computing their standard error of estimates (SEEs) and multiple correlation coefficients ( Rs). With respect to the obtained results, the lowest SEEs (27.81 and 18.73 W) and the highest Rs (0.92 and 0.94) for the most accurate UBP and UBP prediction models have been obtained by SVM using the radial basis function kernel (SVM-RBF), respectively. The predictor variables gender and age have been found to be the most important variables in predicting the UBP and UBP of cross-country skiers. Also, the results have been compared to those of multilayer perceptron and multiple linear regression-based prediction models. Prediction models based on SVM-RBF exhibit much lower SEEs and higher Rs than the prediction models developed by other regression methods and can be safely utilized in making UBP predictions within acceptable limits of accuracy. [ABSTRACT FROM AUTHOR]

Forecasting models for photovoltaic energy production are important tools for managing energy flows. The aim of this study was to accurately predict the energy production of a PV plant in Italy, using a methodology based on support vector machines . The model uses historical data of solar irradiance, environmental temperature and past energy production to predict the PV energy production for the next day with an interval of 15 min. The technique used is based on $$\nu $$ -SVR, a support vector regression model where you can choose the number of support vectors. The forecasts of energy production obtained with the proposed methodology are very accurate, with the $$R^{2}$$ coefficient exceeding 90 % . The quality of the predicted values strongly depends on the goodness of the weather forecast, and the $$R^{2}$$ value decreases if the predictions of irradiance and temperature are not very accurate. [ABSTRACT FROM AUTHOR]

Accurately, forecasting of the flatness plays a highly significant role in the flatness theory and flatness control system, but it is quite difficult and complicated due to the nonlinear characteristics of flatness pattern recognition and lack of available observed data set. Recently, support vector regression (SVR) is being proved an effective machine learning technique for solving nonlinear regression problem with small sample set, because of its nonlinear mapping capabilities. However, it has also been proved that the prediction precision of SVR is highly dependent of SVR parameters, which are hardly choosing for the SVR. As in many excellent algorithms, gravitational search algorithm (GSA) not only has strong global searching capability, but also is very easy to implement. In the paper, an improved gravitational search algorithm (IGSA) is presented to further enhance optimal performance of GSA, and it is employed to serve as a method for pre-selecting SVR parameters. Based on SVR and IGSA algorithms, a forecasting model of flatness pattern recognition is proposed. Where, the IGSA is employed to optimize the parameters of SVR model to determine the parameters as fast and accurate as possible. Afterward, a procedure of forecasting flatness was put forward to evaluate the efficiency of the proposed IGSA-SVR model, which was compared with normal SVR model, IGSA-BP model and extreme learning machine model. The results affirm that the proposed algorithm outperforms other technique. [ABSTRACT FROM AUTHOR]

Precipitation and deposition of asphaltene cause number of severe problems in downstream and upstream of oil industry. Hence, it is essential to present a dependable model for quantitative estimation of asphaltene precipitation. This paper aims to introduce a hybrid support vector regression (SVR) with harmony search (HS) as an intelligence approach to create quantitative formulation between amount of asphaltene precipitation and titration data. Harmony search is combined with SVR for determining the optimal value of its user-defined parameters. The optimization implementation by HS significantly improves the generalization capability of SVR. A dataset that includes 176 data points was employed in the current study, while 141 data points were utilized for constructing the model and the remainder data points (35 data points) were used for assessment of degree of accuracy and robustness. Evaluating the performance of constructed model based on statistical criteria indicates that the hybrid model has acceptable accuracy to estimate the amount of asphaltene precipitation from titration data. This study concludes that optimization of SVR with HS produces a smart hybrid model, which is an appropriate solution for modeling of the asphaltene precipitation. [ABSTRACT FROM AUTHOR]

In this work, we introduce a method for data analysis in nonstationary environments: time-adaptive support vector regression (TA-SVR). The proposed approach extends a previous development that was limited to classification problems. Focusing our study on time series applications, we show that TA-SVR can improve the accuracy of several aspects of nonstationary data analysis, namely the tasks of modelling and prediction, input relevance estimation, and reconstruction of a hidden forcing profile. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel nonparallel hyperplane classifier, named ν-nonparallel support vector machine ( ν-NPSVM), for binary classification. Based on our recently proposed method, i.e., nonparallel support vector machine (NPSVM), which has been proved superior to the twin support vector machines, ν-NPSVM is parameterized by the quantity ν to let ones effectively control the number of support vectors. By combining the ν-support vector classification and the ν-support vector regression together to construct the primal problems, ν-NPSVM inherits the advantages of ν-support vector machine so that enables us to eliminate one of the other free parameters of the NPSVM: the accuracy parameter ε and the regularization constant C. We describe the algorithm, give some theoretical results concerning the meaning and the choice of ν, and also report the experimental results on lots of data sets to show the effectiveness of our method. [ABSTRACT FROM AUTHOR]

This paper presents a data-driven modeling and optimization method for cavity filters, according to a limited amount of measurement data. In the method, a model that reveals the effect of mechanical structure on electrical performance of cavity filters is firstly developed by an improved algorithm, which can increase the modeling accuracy of small data set by incorporating multi-kernel and prior knowledge into the framework of linear programming support vector regression. Then, an approach to optimize the structure of cavity filters is formulated by using the developed data-based model, and the obtained results can assist the fabrication of the same filter in the future. Some experiments from a synthetic example and a practical application of cavity filter have been carried out, and the experimental results confirm the effectiveness of the method. The model is particularly suited to a computer-aided manufacturing of volume-producing filters, and the proposed algorithm shows great potential in some applications where the experimental data are very few and the prior knowledge is available. [ABSTRACT FROM AUTHOR]

As an effective approach for multi-input multi-output regression estimation problems, a multi-dimensional support vector regression (SVR), named M-SVR, is generally capable of obtaining better predictions than applying a conventional support vector machine (SVM) independently for each output dimension. However, although there are many generalization error bounds for conventional SVMs, all of them cannot be directly applied to M-SVR. In this paper, a new leave-one-out (LOO) error estimate for M-SVR is derived firstly through a virtual LOO cross-validation procedure. This LOO error estimate can be straightway calculated once a training process ended with less computational complexity than traditional LOO method. Based on this LOO estimate, a new model selection methods for M-SVR based on multi-objective optimization strategy is further proposed in this paper. Experiments on toy noisy function regression and practical engineering data set, that is, dynamic load identification on cylinder vibration system, are both conducted, demonstrating comparable results of the proposed method in terms of generalization performance and computational cost. [ABSTRACT FROM AUTHOR]

In this paper, we develop multi-layer feed-forward artificial neural network (MFANN) models for predicting the performance measures of a message-passing multiprocessor architecture interconnected by the simultaneous optical multiprocessor exchange bus (SOME-Bus), which is a fiber-optic interconnection network. OPNET Modeler is used to simulate the SOME-Bus multiprocessor architecture and to create the training and testing datasets. The performance of the MFANN prediction models is evaluated using standard error of estimate (SEE) and multiple correlation coefficient ( R). Also, the results of the MFANN models are compared with the ones obtained by generalized regression neural network (GRNN), support vector regression (SVR), and multiple linear regression (MLR). It is shown that MFANN models perform better (i.e., lower SEE and higher R) than GRNN-based, SVR-based, and MLR-based models for predicting the performance measures of a message-passing multiprocessor architecture. [ABSTRACT FROM AUTHOR]

One of the major activities of financial firms and private investors is to predict future prices of stocks. However, stock index prediction is regarded as a challenging task of the prediction problem since the stock market is a complex, chaotic and nonlinear dynamic system. As stock markets are highly dynamic and exhibit wide variation, it may be more realistic and practical that assumed the stock index data are a nonlinear mixture data. In this study, a hybrid stock index prediction model by utilizing nonlinear independent component analysis (NLICA), support vector regression (SVR) and particle swarm optimization (PSO) is proposed. In the proposed model, first, the NLICA is used to deal with the nonlinearity property of the stock index data. The proposed model utilizes NLICA to extract features from the observed stock index data. The features which can be used to represent underlying/hidden information of the data are then served as the inputs of SVR to build the stock index prediction model. Finally, PSO is applied to optimize the parameters of the SVR prediction model since the parameters of SVR must be carefully selected in establishing an effective and efficient SVR model. In order to evaluate the performance of the proposed approach, the closing indexes of the Taiwan stock exchange capitalization weighted stock index, Shanghai stock exchange composite index and Bombay stock exchange index are used as illustrative examples. Experimental results showed that the proposed hybrid stock index prediction method significantly outperforms the other six comparison models. It is an efficient and effective alternative for stock index forecasting. [ABSTRACT FROM AUTHOR]

Stock index forecasting is one of the most difficult tasks that financial organizations, firms and private investors have to face. Support vector regression (SVR) has become a popular alternative in stock index forecasting tasks due to its generalization capability in obtaining a unique solution. However, the major limitation of SVR is that it cannot capture the relative importance of independent variables to the dependent variable when many potential independent variables are considered. This study incorporates feature selection method and SVR for building stock index forecasting model. The proposed model uses multivariate adaptive regression splines (MARS), an effective nonlinear and nonparametric regression methodology, to identify important forecasting variables. The obtained significant predictor variables are then served as the inputs for the SVR model. Experimental results reveal that the obtained important variables from MARS can improve the forecasting performance of the SVR models. Moreover, the MARS results provide useful information about the relationship between the selected predictor variables and stock index through the obtained basis functions, important predictor variables and the MARS prediction function. Hence, the proposed stock index forecasting model can generate good forecasting performance and exhibits the capability of identifying significant predictor variables, which provide valuable information for further investment decisions/strategies. [ABSTRACT FROM AUTHOR]

This study proposes a new regressor-ε-twin support vector regression (ε-TSVR) based on TSVR. ε-TSVR determines a pair of ε-insensitive proximal functions by solving two related SVM-type problems. Different form only empirical risk minimization is implemented in TSVR, the structural risk minimization principle is implemented by introducing the regularization term in primal problems of our ε-TSVR, yielding the dual problems to be stable positive definite quadratic programming problems, so can improve the performance of regression. In addition, the successive overrelaxation technique is used to solve the optimization problems to speed up the training procedure. Experimental results for both artificial and real datasets show that, compared with the popular ε-SVR, LS-SVR and TSVR, our ε-TSVR has remarkable improvement of generalization performance with short training time. [ABSTRACT FROM AUTHOR]

In this paper, a simple and linearly convergent Lagrangian support vector machine algorithm for the dual of the twin support vector regression (TSVR) is proposed. Though at the outset the algorithm requires inverse of matrices, it has been shown that they would be obtained by performing matrix subtraction of the identity matrix by a scalar multiple of inverse of a positive semi-definite matrix that arises in the original formulation of TSVR. The algorithm can be easily implemented and does not need any optimization packages. To demonstrate its effectiveness, experiments were performed on well-known synthetic and real-world datasets. Similar or better generalization performance of the proposed method in less training time in comparison with the standard and twin support vector regression methods clearly exhibits its suitability and applicability. [ABSTRACT FROM AUTHOR]

A feature-weighted Support Vector Machine regression algorithm is introduced in this paper. We note that the classical SVM is based on the assumption that all the features of the sample points supply the same contribution to the target output value. However, this assumption is not always true in real problems. In the proposed new algorithm, we give different weight values to different features of the samples in order to improve the performance of SVM. In our algorithm, firstly, a measure named grey correlation degree is applied to evaluate the correlation between each feature and the target problem, and then the values of the grey correlation degree are used as weight values assigned to the features. The proposed method is tested on sample stock data sets selected from China Shenzhen A-share market. The result shows that the new version of SVM can improve the accuracy of the prediction. [ABSTRACT FROM AUTHOR]

This paper deals with the application of a support vector machine (SVM) optimization technique to predict the blast-induced ground vibration. Peak particle velocity (PPV) is an important parameter to be kept under control to minimize the damage caused due to the ground vibration. A number of previous researchers have tried to use different empirical methods to predict PPV, but these empirical equations have their limitations due to its less versatile application and acceptability from field conditions. Therefore, it is difficult to apply these empirical equations to predict PPV because they are based on limited parameters which does not really reflect and connect with real influencing parameters. In this paper, SVM technique is used for the prediction of PPV by incorporating blast design and explosive parameters, and the suitability of one technique over other has been tested based on the results. To avoid the biasness in man-made choice of parameters of SVM, we have used the chaos optimization algorithm to find the optimal parameters which can help the model to enhance the learning efficiency and capability of prediction. Datasets have been obtained from one of the large opencast mine from southeastern coalfield limited, Chhattisgarh, India. One hundred and twenty-seven datasets were used to establish SVM architecture, and 10 datasets have been randomly chosen for validation of SVM model to see its prediction potential. The results obtained have been compared with different vibration predictors, multivariate regression analysis, artificial neural network and the superiority of application on SVM over previous methodology. The mean absolute percentage error using SVM is very low (0.001) as compared to other predictors indicate its better prediction capability. [ABSTRACT FROM AUTHOR]

In this paper, extreme learning machine (ELM) for ε-insensitive error loss function-based regression problem formulated in 2-norm as an unconstrained optimization problem in primal variables is proposed. Since the objective function of this unconstrained optimization problem is not twice differentiable, the popular generalized Hessian matrix and smoothing approaches are considered which lead to optimization problems whose solutions are determined using fast Newton-Armijo algorithm. The main advantage of the algorithm is that at each iteration, a system of linear equations is solved. By performing numerical experiments on a number of interesting synthetic and real-world datasets, the results of the proposed method are compared with that of ELM using additive and radial basis function hidden nodes and of support vector regression (SVR) using Gaussian kernel. Similar or better generalization performance of the proposed method on the test data in comparable computational time over ELM and SVR clearly illustrates its efficiency and applicability. [ABSTRACT FROM AUTHOR]

In this paper, a novel regression algorithm coined flexible support vector regression is proposed. We first model the insensitive zone in classic support vector regression, respectively, by its up- and down-bound functions and then give a kind of generalized parametric insensitive loss function (GPILF). Subsequently, based on GPILF, we propose an optimization criterion such that the unknown regressor and its up- and down-bound functions can be found simultaneously by solving a single quadratic programming problem. Experimental results on both several publicly available benchmark data sets and time series prediction show the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, an attempt has been made to predict the thermal conductivity (TC) of rocks by incorporating uniaxial compressive strength, density, porosity, and P-wave velocity using support vector machine (SVM). Training of the SVM network was carried out using 102 experimental data sets of various rocks, whereas 25 new data sets were used for the testing of the TC by SVM model. Multivariate regression analysis (MVRA) has also been carried out with same data sets that were used for the training of SVM model. SVM and MVRA results were compared based on coefficient of determination (CoD) and mean absolute error (MAE) between experimental and predicted values of TC. It was found that CoD between measured and predicted values of TC by SVM and MVRA was 0.994 and 0.918, respectively, whereas MAE was 0.0453 and 0.2085 for SVM and MVRA, respectively. [ABSTRACT FROM AUTHOR]

Twin support vector regression (TSVR) was proposed recently as a novel regressor that tries to find a pair of nonparallel planes, i.e., ε-insensitive up- and down-bounds, by solving two related SVM-type problems. However, it may incur suboptimal solution since its objective function is positive semi-definite and the lack of complexity control. In order to address this shortcoming, we develop a novel SVR algorithm termed as smooth twin SVR (STSVR). The idea is to reformulate TSVR as a strongly convex problem, which results in unique global optimal solution for each subproblem. To solve the proposed optimization problem, we first adopt a smoothing technique to convert the original constrained quadratic programming problems into unconstrained minimization problems, and then use the well-known Newton-Armijo algorithm to solve the smooth TSVR. The effectiveness of the proposed method is demonstrated via experiments on synthetic and real-world benchmark datasets. [ABSTRACT FROM AUTHOR]

This paper improves the recently proposed twin support vector regression (TSVR) by formulating it as a pair of linear programming problems instead of quadratic programming problems. The use of 1-norm distance in the linear programming TSVR as opposed to the square of the 2-norm in the quadratic programming TSVR leads to the better generalization performance and less computational time. The effectiveness of the enhanced method is demonstrated by experimental results on artificial and benchmark datasets. [ABSTRACT FROM AUTHOR]

Sudden changes in weather, in particular extreme temperatures, can result in increased energy expenditures, depleted agricultural resources, and even loss of life. However, these ill effects can be reduced with accurate air temperature predictions that provide adequate advance warning. Support vector regression (SVR) was applied to meteorological data collected across the state of Georgia in order to produce short-term air temperature predictions. A method was proposed for reducing the number of training patterns of massively large data sets that does not require lengthy pre-processing of the data. This method was demonstrated on two large data sets: one containing 300,000 cold-weather training patterns collected during the winter months and one containing 1.25 million training patterns collected throughout the year. These patterns were used to produce predictions from 1 to 12 h ahead. The mean absolute error (MAE) for the evaluation set of winter-only patterns ranged from 0.514°C for the 1-h prediction horizon to 2.303°C for the 12-h prediction horizon. For the evaluation set of year-round patterns, the MAE ranged from 0.513°C for the 1-h prediction horizon to 1.922°C for the 12-h prediction horizon. These results were competitive with previously developed artificial neural network (ANN) models that were trained on the full data sets. For the winter-only evaluation data, the SVR models were slightly more accurate than the ANN models for all twelve of the prediction horizons. For the year-round evaluation data, the SVR models were slightly more accurate than the ANN models for three of the twelve prediction horizons. [ABSTRACT FROM AUTHOR]

In this paper, we propose a Newton iterative method of solution for solving an ε-insensitive support vector regression formulated as an unconstrained optimization problem. The proposed method has the advantage that the solution is obtained by solving a system of linear equations at a finite number of times rather than solving a quadratic optimization problem. For the case of linear or kernel support vector regression, the finite termination of the Newton method has been proved. Experiments were performed on IBM, Google, Citigroup and Sunspot time series. The proposed method converges in at most six iterations. The results are compared with that of the standard, least squares and smooth support vector regression methods and of the exact solutions clearly demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Although the solution of support vector machine is relatively sparse, it makes unnecessarily liberal use of basis functions since the number of support vectors required typically grows linearly with the size of the training set. In this paper, we present a simple post-processing method to sparsify the solution of support vector regression (SVR). The main idea is as follows: first, we train a SVR machine on the full training set; then another SVR machine is trained only on a subset of the full training set with modified target values. This process is done several times iteratively. Experiments indicate that the proposed method can reduce the support vectors greatly while maintaining the good generalization capacity of SVR. [ABSTRACT FROM AUTHOR]

The main aim of this paper is to predict NO and NO2 concentrations 4 days in advance by comparing two artificial intelligence learning methods, namely, multi-layer perceptron and support vector machines, on two kinds of spatial embedding of the temporal time series. Hourly values of NO and NO2 concentrations, as well as meteorological variables were recorded in a cross-road monitoring station with heavy traffic in Szeged, in order to build a model for predicting NO and NO2 concentrations several hours in advance. The prediction of NO and NO2 concentrations was performed partly on the basis of their past values, and partly on the basis of temperature, humidity and wind speed data. Since NO can be predicted more accurately, its values were considered primarily when forecasting NO2. Time series prediction can be interpreted in a way that is suitable for artificial intelligence learning. Two effective learning methods, namely, multi-layer perceptron and support vector regression are used to provide efficient non-linear models for NO and NO2 time series predictions. Multi-layer perceptron is widely used to predict these time series, but support vector regression has not yet been applied for predicting NO and NO2 concentrations. Three commonly used linear algorithms were considered as references: 1-day persistence, average of several day persistence and linear regression. Based on the good results of the average of several day persistence, a prediction scheme was introduced, which forms weighted averages instead of simple ones. The optimization of these weights was performed with linear regression in linear case and with the learning methods mentioned in non-linear case. Concerning the NO predictions, the non-linear learning methods give significantly better predictions than the reference linear methods. In the case of NO2, the improvement of the prediction is considerable, however, it is less notable than for NO. [ABSTRACT FROM AUTHOR]