Support Vector Machines (SVMs) represent a robust and versatile class of machine learning algorithms that have significantly shaped the fields of pattern recognition, classification, and regression.

Other common machine learning regression algorithms (short of neural networks) include Naive Bayes, Decision Tree, K-Nearest Neighbors, LVQ (Learning Vector Quantization), LARS Lasso, Elastic Net ...

Xiang Zhang, Yichao Wu, Lan Wang, Runze Li, Variable selection for support vector machines in moderately high dimensions, Journal of the Royal Statistical Society.

He’s credited with coming up with the first support vector machine (SVM) algorithm. SVMs are widely used today for machine learning purposes.

In this paper, a novel hybrid approach integrating genetic algorithm (GA) and support vector machines (SVM) is proposed to conduct the key factor exploration tasks in the core competitiveness ...

Support vector machines (SVM): SVM is a powerful classification and regression algorithm that performs well in dealing with non-linear relationships.