The artificial intelligence method used here is support vector regression (SVR). The original data are mapped into a high-dimensional "feature space" where the cross product is defined in terms of a kernel function. In the current approach, the radial basis kernel function is used. Three hyper...
Support Vector Machines (SVM) are widely used in machine learning for classification problems, but they can also be applied toregressionproblems through Support Vector Regression (SVR). SVR uses the same principles as SVM but focuses on predicting continuous outputs rather than classifying data points...
The hyper-parameters of support vector regression influence the performance of its model.In the normal gradient descent method,kernel functions or estimati... YN Guo,Y Mei,DW Xiao - 《International Journal of Intelligent Computing & Cybernetics》 被引量: 6发表: 2010年 Linear and nonlinear structu...
One of the advantages ofSupport Vector Machine, andSupport Vector Regressionas the part of it, is that it can be used to avoid difficulties of using linear functions in the high dimensional feature space and optimization problem is transformed into dual convex quadratic programmes. In regression ca...
2012, IEEE Transactions on Geoscience and Remote Sensing A binary-encoded tabu-list genetic algorithm for fast support vector regression hyper-parameters tuning 2011, International Conference on Intelligent Systems Design and Applications, ISDA View all citing articles on ScopusView...
Mdl = RegressionSVM ResponseName: 'Y' CategoricalPredictors: [] ResponseTransform: 'none' Alpha: [75x1 double] Bias: 57.3800 KernelParameters: [1x1 struct] NumObservations: 94 BoxConstraints: [94x1 double] ConvergenceInfo: [1x1 struct] IsSupportVector: [94x1 logical] Solver: 'SMO' The Comm...
Support Vector Regressionis an extension of SVM which introduces a region, named tube, around the function to optimize with the aim of finding the tube that best approximates the continuous-valued function, while minimizing the prediction error, that is, the difference between the predicted and th...
Mdl = RegressionSVM ResponseName: 'Y' CategoricalPredictors: [] ResponseTransform: 'none' Alpha: [75x1 double] Bias: 57.3800 KernelParameters: [1x1 struct] NumObservations: 94 BoxConstraints: [94x1 double] ConvergenceInfo: [1x1 struct] IsSupportVector: [94x1 logical] Solver: 'SMO' Mdl is...
Figure 4. Particle swarm optimization–support vector regression implementation process. 3.1.1. Particle Swarm Optimization Hyperparameters are parameters set manually before the algorithm to control the behavior and performance of the model. To avoid the arbitrariness of hyperparameter selection, a parti...
5.4. Hyperparameters Explanation The C parameter is a scalar that controls the penalty for the values that deviate from the predicted regression curve. The higher the value C is, the more noticeable the error is [12]. In contrast, a smaller value of C allows for a larger margin and more...