Ensemble SVM Regression Based Multi-View Face Detection System

Support Vector Machine (SVM) for regression has recently attracted growing research interest due to its obvious advantage such as nonlinear function approximation with arbitrary accuracy, and good generalization ability, unique and globally optimal solutions. An overview of the basic ideas underlying SVM for regression is given in this paper. In particular, new methods such as n-SVM, LS-SVM, weighted SVM and linear SVM, and optimization algorithms including decomposition method and SMO and incremental learning with fast computational speed and ease of implementation are concentrated as well. SVM for regression is an important and promising new direction in the area of nonlinear parameter identification, forecast, modeling and control.

Leaf area index (LAI) is an important biophysical trait for forest ecosystem and ecological modeling, as it plays a key role for the forest productivity and structural characteristics. The ground-based methods like the handheld optical instruments for predicting LAI are subjective, pricy and time-consuming. The advent of very high spatial resolutions multispectral data and robust machine learning regression algorithms like support vector machines (SVM) and artificial neural networks (ANN) has provided an opportunity to estimate LAI at tree species level. The objective of the this study was therefore to test the utility of spectral vegetation indices (SVI) calculated from the multispectral WorldView-2 (WV-2) data in predicting LAI at tree species level using the SVM and ANN machine learning regression algorithms. We further tested whether there are significant differences between LAI of intact and fragmented (open) indigenous forest ecosystems at tree species level. The study shows that LAI at tree species level could accurately be estimated using the fragmented stratum data compared with the intact stratum data. Specifically, our study shows that the accurate LAI predictions were achieved for Hymenocardia ulmoides using the fragmented stratum data and SVM regression model based on a validation dataset (R2Val = 0.75, RMSEVal = 0.05 (1.37% of the mean)). Our study further showed that SVM regression approach achieved more accurate models for predicting the LAI of the six endangered tree species compared with ANN regression method. It is concluded that the successful application of the WV-2 data, SVM and ANN methods in predicting LAI of six endangered tree species in the Dukuduku indigenous forest could help in making informed decisions and policies regarding management, protection and conservation of these endangered tree species.

With the wide application of support vector machine(SVM), the algorithm of using online learning for realizing regression had been developed furtherly. After the mathematical mode of direct support vector machine (DSVM) for regression was introduced which was of the learning capacity that was similar to least squares support vector machine but less complexity of computation, according to Cholesky factorization, the algorithm of incremental learning and decremental learning were designed for DSVM in this paper, through them online learning that based on time window for regression was realized. Experimental results of simulation through Mackey-Glass chaotic time series and pseudo periodic synthetic time series data set all indicate the feasibility of the learning algorithm which will be beneficial for SVM's application in depth.

Towards an effective cross-task mental workload recognition model using electroencephalography based on feature selection and support vector machine regression

Data-driven regression methods are adept at modeling the spindle thermal elongation for error reduction purposes, but are limited in their generalization ability for the established model to take effect under different work conditions, where one prime cause is that the measurement errors and environmental disturbances would inevitably lead to successive outliers in the experimental training data, and the regression algorithms tend to over-fit to such outliers in the thermal datasets which are usually small. Here, an approach is proposed for the first time to detect and remove the suspected outliers in the spindle thermal data while preserve most of the informative data at the same time, so that the disturbances from the outliers can be minimized. The proposed approach for outliers detection is a combination of Boosting and SVR (BSOD), where the support vector machine for regression (SVR) is used as the weak learner in Boosting. Furthermore, the SVR, which is now widely considered as the most suitable regression tool for mapping the temperature-thermal elongation relationship for a spindle, is adopted to regression model the spindle thermal elongation, where the genetic algorithm (GA) is employed to determine the optimal parameter setting for the modeling. Effectiveness of the BSOD approach was verified using four experimental datasets from two different spindles of precision boring machines under different work conditions. The results showed that the BSOD approach reduced the mean squared error MSE of the genetic algorithm optimized support vector machine estimation for unseen thermal elongation datasets in two cases by 66.09% and 49.13%, respectively. This research provides a feasible way to enhance the modeling quality for the thermal elongation of spindles during working process.

Short-term stock price forecasting using kernel principal component analysis and support vector machines: the case of Casablanca stock exchange

Ensemble approach based on bagging, boosting and stacking for short-term prediction in agribusiness time series

BackgroundThis study analyzed the influencing factors of fetal growth restriction (FGR), and selected epidemiological and fetal parameters as risk factors for FGR.ObjectiveTo establish a dynamic prediction model of FGR.MethodsThis study used two methods, support vector machine (SVM) and multivariate logistic regression, to establish the prediction model of FGR at different gestational weeks.ResultsAt 20–24 weeks and 25–29 weeks of gestation, the effect of the multivariate Logistic method on model prediction was better. At 30–34 weeks of gestation, the prediction effect of FGR model using the SVM method is better. The ROC curve area was above 85%.ConclusionsThe dynamic prediction model of FGR based on SVM and logistic regression is helpful to improve the sensitivity of FGR in pregnant women during prenatal screening. The establishment of prediction models at different gestational ages can effectively predict whether the fetus has FGR, and significantly improve the clinical treatment effect.

Support Vector Machines for Regression (SVR) proved to perform well. However, they are not preferred in image analysis due to a high number of needed support vectors (SV) and consequently long processing times. We present a method for simplifying the original SVR regression function up to a user-specified degree of accepted performance decrease. We show results for two regression problems: modelling leaf area index and dry vegetation mixing fraction using simulated hyperspectral EnMAP data. In both cases, SVR demonstrate high potential for modelling complex dependencies between hyperspectral reflectance and quantitative targets. By simplifying the original SVR, we observed reduction rates in number of SV in the 86% to 95% range for acceptable degrees of approximation quality. This enables a fast mapping of complete EnMAP scenes.

This study investigates the use of machine learning algorithms to predict the plastic rotational capacity of reinforced concrete elements, a critical factor in assessing the response and vulnerability of structures under seismic loads. Traditional mathematical models developed to evaluate the rotational capacity of concrete elements are insufficient for accurately capturing the complex nonlinear behavior of structures under seismic loads. Machine learning offers a promising alternative, given its ability to model nonlinear relationships and improve predictive accuracy based on diverse datasets. The study compares the performance of six machine learning algorithms - Linear Regression (LR), Support Vector Machine for Regression (SVR), Least Squares Support Vector Machine (LS-SVM), Decision Tree (DT), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) - using datasets of reinforced concrete columns and walls. Results reveal that SVR and XGBoost provide the most accurate predictions for columns, with R² values up to 91%, while XGBoost performs best for walls with a maximum R² of 78%. However, the accuracy of these models is limited by the availability and diversity of the datasets, particularly for elements prone to shear failure. This research underscores the potential of machine learning in seismic design and structural assessment, though it also highlights the need for more comprehensive data to enhance model accuracy.

Controlling factors and predictions of well-connected pore-throat volumes in tight oil sandstones

Quantifying stand volume through open-access satellite remote sensing data supports proper management of forest stand. Because of limitations on single sensor and support vector machine for regression (SVR) as well as benefits from hybrid models, this study innovatively builds a hybrid model as support vector machine for regression kriging (SVRK) to map stand volume of the Changbai Mountains mixed forests covering 171,450 ha area based on a small training dataset (n = 928). This SVRK model integrated SVR and its residuals interpolated by ordinary kriging. To determine the importance of multi-sensor predictors from ALOS and Sentinel series, the increase in root mean square error (RMSE) of SVR was calculated by removing the variable after the standardization. The SVRK model achieved accuracy with mean error, RMSE and correlation coefficient in –2.67%, 25.30% and 0.76, respectively, based on an independent dataset (n = 464). The SVRK improved the accuracy of 9% than SVR based on RMSE values. Topographic indices from L band InSAR, backscatters of L band SAR, and texture features of VV channel from C band SAR, as well as vegetation indices of the optical sensor were contributive to explain spatial variations of stand volume. This study concluded that SVRK was a promising approach for mapping stand volume in the heterogeneous temperate forests with limited samples.

Local spatial properties based image interpolation scheme using SVMs

Modeling the behavior of groundwater levels is necessary to implement sustainable groundwater resource management. Groundwater is a non-linear and complex system, which can be modeled by data-driven models. This study evaluates the performances of data-driven models, support vector machine regression (SVR) and artificial neural network (ANN), for forecasting groundwater levels of confined and unconfined systems at 1-, 2-, and 3-month ahead. This is the first time that confined and unconfined aquifers have been compared using data-driven models. In addition, to identify the optimal input combination, a hybrid gamma test (GT) and genetic algorithm (GA) was used. The coefficient of correlation (R), Mean Absolute Error (MAE), root mean squared error (RMSE), Nash–Sutcliffe efficiency (NSE), and developed discrepancy ratio (DDR) were applied to evaluate the SVR and ANN models. Results showed that the SVR and ANN models were more accurate for the unconfined system than the confined system for forecasts up to 3-month ahead. In both hydrogeological systems for 1-month ahead, the models performed better than for 2- and 3-month ahead forecasts, and the accuracy of the models decreased as the months ahead increased. The SVR model performed better than the ANN model for 1-, 2-, and 3-month ahead groundwater-level forecasting. The SVR model could be successfully used in predicting monthly groundwater in confined and unconfined systems.

On the optimization of the support vector machine regression hyperparameters setting for gas sensors array applications