View source for Linear Kernel-based Support Vector Machine Algorithm

A [[Linear Kernel-based Support Vector Machine Algorithm]] is an [[SVM training algorithm]] that can be implemented by a [[linear SVM training system]] to solve a [[linear SVM training task]] (to produce a [[linear SVM]] based on a [[linear kernel]]).
* <B>Context:</B>
** It can be implement in a [[Linear SVM System]] (that solves a [[linear SVM task]]).
** It can be interpreted as maximizing the margin of a [[Linear Kernel]] (so that the distance to the closest [[misclassified entity]] is the widest).
** It can have a [[Linear SVM Computational Complexity]], such as: <math><math>\mathcal{O}(m^2\cdot n)</math> ([[runtime computational complexity]]).
* <B>Example(s):</B>
** …
* <B>Counter-Example(s):</B>
** [[Non-Linear SVM Algorithm]].
** [[Logistic Regression Algorithm]].
* <B>See:</B> [[Kernel-based SVM Algorithm]], [[Discriminative Linear Classifier Learning with a Convex Loss Function]], [[Linearly Separable]], [[Quadratic Programming]], [[Lagrange Multiplier]], [[Karush–Kuhn–Tucker Conditions]], [[Bias of an Estimator]], [[Dual Problem]], [[Maximum-Margin Hyperplane]].

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