Support Vector Machines (page 1 of 10)

The support vector machine (SVM) machine learning algorithm is used for supervised machine learning problems predicting known targets. This algorithm may be used for either regression or classification problems, but it is primarily used for classification problems. The SVM method was developed by Vladimir Vapnik in the 1970s. In the 1990s, the kernel method was developed, which made it possible to solve non-linear problems efficiently using SVM. Even with the advancement of neural networks, SVMs are still important, flexible algorithms used to train models that balance bias and variance.