Online learning algorithm of direct support vector machine for regression based on Cholesky factorization

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.

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.

In this paper, we present a novel learning method for SVM (Support Vector Machine) regression ensemble used in multi-pose face detection. Firstly, several view-specific SVM classifiers are trained by using corresponding positive and negative examples. And then, an ensemble mechanism (SVM regression) is used to combine the results from the view-specific SVCs (Support Vector Classifiers). Experimental results show that the detection accuracy of the ensemble is better than the view-specific SVCs. Moreover, the SVR ensemble does not need extra pose estimation process prior to the classification; it generates pose information in addition to its detection results.

Aim: prediction of coronary disease using novel support vector machine and comparing its accuracy with logistic regression algorithm. Materials and methods: Two social affairs are proposed for predicting the accuracy (%) of coronary disease. To be explicit, the novel supports vector machine and logistic regression algorithms. Here we take 20 samples each for appraisal and compare. The sample size was calculated using G power with pretest power at 80% and the alpha of 0.05 value. Result: The logistic regression gives better precision (87.82%) than the novel support vector machine (SVM) accuracy (81.30%). Thus the real significance of logistic regression is better than novel support vector machine algorithms. Conclusion: From the result, it might be gathered that logistic regression helps in expecting the coronary sickness with more accuracy to appear differently in relation to novel support vector machine algorithms.

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

The main problem in producing high accuracy software defect prediction is if the data set has an imbalance class and dichotomous characteristics. The imbalanced class problem can be solved using a data level approach, such as resampling methods. While the problem of software defects predicting if the data set has dichotomous characteristics can be approached using the classification method. This study aimed to analyze the performance of the proposed software defect prediction method to identify the best combination of resampling methods with the appropriate classification method to provide the highest accuracy. The combination of the proposed methods first is the resampling process using oversampling, under-sampling, or hybrid methods. The second process uses the classification method, namely the Support Vector Machine (SVM) algorithm and the Logistic Regression (LR) algorithm. The proposed, tested model uses five NASA MDP data sets with the same number attributes of 37. Based on the t-test, the < = 0.0344 < 0.05 and the > = 3.1524 > 2.7765 which indicates that the combination of the proposed methods is suitable for classifying imbalanced class. The performance of the classification algorithm has also improved with the use of the resampling process. The average increase in AUC values using the resampling in the SVM algorithm is 17.19%, and the LR algorithm is at 7.26% compared to without the resampling process. Combining the three resampling methods with the SVM algorithm and the LR algorithm shows that the best combining method is the oversampling method with the SVM algorithm to software defects prediction in imbalanced class with an average accuracy value of 84.02% and AUC 91.65%.

Text categorization is a supervised learning task which aims to assign labels to documents based on the predicted outcome suggested by a classifier trained on a set of labelled documents. The association of text classification to facilitate labelling reports/complaints in the economic and health related fields can have a tremendous impact in the speed at which these are processed, and therefore, lowering the required time to act upon these complaints and reports. In this work, we aim to classify complaints into the main 4 economic activities given by the Portuguese Economic and Food Safety Authority. We evaluate the classification performance of 9 algorithms (Complement Naive Bayes, Bernoulli Naive Bayes, Multinomial Naive Bayes, K-Nearest Neighbors, Decision Tree, Random Forest, Support Vector Machine, AdaBoost and Logistic Regression) at different layers of text preprocessing. Results reveal high levels of accuracy, roughly around 85%. It was also observed that the linear classifiers (support vector machine and logistic regression) allowed us to obtain higher f1-measure values than the other classifiers in addition to the high accuracy values revealed. It was possible to conclude that the use of these algorithms is more adequate for the data selected, and that applying text classification methods can facilitate and help the complaints and reports processing which, in turn, leads to a swifter action by authorities in charge. Thus, relying on text classification of reports and complaints can have a positive influence in either economic crime prevention or in public health, in this case, by means of food-related inspections.

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.

Most of the approaches to digital communication applications using support vector machines (SVMs) rely on the conventional classification and regression SVM algorithms. However, the introduction of complex algebra in the SVM formulation can provide us with a more flexible and natural framework when dealing with complex constellations and symbols. In this chapter, an SVM algorithm for coherent robust demodulation in orthogonal frequency division multiplexing (OFDM) systems is studied. We present a complex regression SVM formulation specifically adapted to a pilot-based OFDM signal, which provides us with a simpler scheme than an SVM multiclassification method. The feasibility of this approach is substantiated by computer simulation results obtained for Institute of Electrical and Electronic Engineers (IEEE) 802.16 broadband fixed wireless channel models. These experiments allow us to scrutinize the performance of the OFDM-SVM system and the suitability of the e-Huber cost function in the presence of non-Gaussian impulse noise interfering with OFDM pilot symbols.

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

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.

In this paper, the titanium alloy milling process is analysized by finite element method, and the processing quality of this materials will be affected by the milling force. A milling force prediction model was established based on support vector machine (SVM) and ant colony optimization (ACO) for titanium alloy milling process cutting parameters. The main feature of this model design methodology is to hybridize the solution construction mechanism of ant colony optimization (ACO) with support vector machine (SVM) regression based on HYPERLINK "https://cn.mathworks.com/discovery/supervised-learning.html" supervised learning algorithm. The results show that this methodology is very efficient, and thus can be used in the machining process parameters optimum and other material processing fields.

According to the characteristics of communication traffic,a traffic forecasting model based on multiple Support Vector Machines(SVM) regression functions was proposed.In this model,the traffic data will be divided into two groups by the date,which are the working-day traffic data and the weekend traffic data.Then two different SVM models are trained using those data.The experimental results show that this model is very effective.Moreover,the performance of this model outperforms that of traditional ARMA model.

Lately, many notorious financial distress and bankruptcy events occurred in the world economic. As we known, bankruptcy of Lehman Brothers Holdings Inc. (LEH) is the largest bankruptcy filing in U.S. history in 2008. These events have serious impacted on the socio-economic and investment in public wealth. Due to solve this dilemma, this research collected 68 listed companies as the raw data from Taiwan Stock Exchange Corporation (TSEC). The support vector machine (SVM) and support vector regression (SVR) techniques were used to implement the financial distress prediction model. Moreover, we adopted a total of 22 ratios which composed of 13 financial ratios and 9 macroeconomic indexes to be the input variables for these models. Finally, the experiments obtained the accuracy rate, Type II error rate and RMSE (root mean squared error) of these classification methods for the financial distress and bankruptcy prediction.