A support vector machine (SVM) optimized by genetic algorithm (GA)-based damage identification method is proposed in this paper. The best kernel parameters are obtained by GA from selection, crossover and mutation, and utilized as the model parameters of SVM. The combined vector of mode shape ...
Among many techniques of classification the support vector machine (SVM) is widely applied to bioinformatics or genetic analysis, because it gives sound theoretical background and its performance is superior to other methods. The SVM can be rewritten by a combination of the hinge loss function and...
The main structure of this paper is as follows: “Support vector machine” introduces the basic theory of SVM; “Improved squirrel search algorithm” presents the basic theory of SSA and its improvement strategies, and tests the optimization performance of MISSA; “Prediction model of PV power gen...
The present work employs the Support Vector Machine (SVM) algorithms as the image segmentation method to guide the numerical model generation. SVM is a classification-based machine learning algorithm built on solid mathematical foundation and optimization frameworks [17,18]. Compared to other supervised...
Important support vector machine vocabulary C parameter A C parameter is a primary regularization parameter in SVMs. It controls the tradeoff between maximizing the margin and minimizing the misclassification of training data. A smaller C enables more misclassification, while a larger C imposes a strict...
The original support vector machine (SVM) uses the hinge loss function, which is non-differentiable and makes the problem difficult to solve in particular for regularized SVM, such as with ℓ1-regularized. On the other hand, the hinge loss is sensitive to noise. To circumvent these drawbacks...
In our study, the support vector machine (SVM) regression model is used to handle the challenge of adjusting the risk factors attached to the patients. Further, the design of exponentially weighted moving average (EWMA) control charts is proposed based on the residuals obtained through SVM ...
We will now examine the properties of the proposed large-scale pinball twin support vector machine (LPTWSVM) in more detail. 4.1 Noise insensitivity For simplicity, we will discuss the noise sensistivity with respect to the linear LPTWSVM problem (21). However, a similar analysis is also app...
We present a Support Vector Machine (SVM) approach to the localization of hazardous particulate releases in an urban area using features constructed only from measurements obtained from a network of sensors. We find high levels of localization accuracy w
Support Vector Machine (SVM) is a powerful, state-of-the-art algorithm with strong theoretical foundations based on the Vapnik-Chervonenkis theory. SVM has strong regularization properties. Regularization refers to the generalization of the model to new data. ...