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.

The Data Science doctor delves into supporting vector machines, software systems that can perform binary classification such as creating a model to predict the gender of a person based on their age, ...

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

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 ...

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 ...

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

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