Support Vector Machine (SVM) is an implementation of the latest generation of machine learning algorithms based on recent advances in statistical learning. These supervised methods are used for classification and regression. SVM is one of the classifiers that I’m going to use in my project to predict if a image has a close-up view of an anchor.
In simple words, given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other. Intuitively, an SVM model is a representation of the examples as points in space although in this specific example there are images, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. New images are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall on.