Support Vector Regression

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An evaluation of adaptive surrogate modeling based optimization with two benchmark problems (2014, background): “Support Vector Machines (SVMs) are learning machines implementing the structural risk minimization inductive principle to obtain good generalization on a limited number of learning patterns (Basak et al., 2007).”
A Novel Approach for Prediction of Monthly Ground Water Level Using a Hybrid Wavelet and Non-Tuned Self-Adaptive Machine Learning Model (2019, methodology): “Vapnik (1998) for applying the support vector machine in regression problems through the implementation of an error function ignoring errors in a specified distance from actual values (i.e. ε-Insensitive) (Basak et al. 2007).”
A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model (2020, methodology): “For estimating problems, Cortes and Vapnik [68] introduced SVM with the capability of wide application as a benchmark machine learning approach.”
Temporal convolutional neural (TCN) network for an effective weather forecasting using time-series data from the local weather station (2020, background): “Moreover, the standard regression and the SVR do not encode sequential information, while LSTM and TCN encode both multivariate and sequential information by taking them into another dimension in the input data (Bai et al. 2018; Basak et al. 2007; Jozefowicz et al. 2015).”