Also known as: Kernel Support Vector Machines (KSVMs) · KSVMs · Kernel Support Vector Machines KSVMs
Kernel support vector machines, или KSVMs, — машины опорных векторов, использующие ядерные функции для создания нелинейных границ решений. Они разделяют классы путем поиска максимального зазора в неявном пространстве признаков более высокой размерности.
A classification algorithm that seeks to maximize the margin between positive and negative classes by mapping input data vectors to a higher dimensional space. For example, consider a classification problem in which the input dataset has a hundred features. To maximize the margin between positive and negative classes, a KSVM could internally map those features into a million-dimension space. KSVMs uses a loss function called hinge loss.