Estimating The Religion of Countries According to Shapes of The Flags Using Support Vector Machines and Kernel Methods
Taha Eren Sarnıç
Support vector machines are a set of related supervised learning method used for classification and regression. In simple words, given a set of training examples, each marked as belonging to one of two categories, a SVM (Support Vector Machines) 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, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall on.
The categorization of the new examples are made with hyperplane. As it is seen in below, two classes are seperated with multiple straight lines.
Pages: 306 – 311 | Full PDF Paper