Kernel Support Vector Machines
A classification algorithm that seeks to maximize the margin between positive and negative classes by mapping input data vectors to a higher dimensional space.
Plain English Explanation
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.
How is it used?
Practitioners refer to kernel support vector machines when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.