Regularization perspectives on support vector machines: Difference between revisions

Revision as of 19:07, 4 August 2022

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Within [[mathematical analysis]], '''Regularization perspectives on support-vector machines''' provide a way of interpreting [[support-vector machine]]s (SVMs) in the context of other regularization-based machine-learning algorithms. SVM algorithms categorize binary data, with the goal of fitting the [[training set]] data in a way that minimizes the average of the hinge-loss function and L2 norm of the learned weights. This strategy avoids [[overfitting]] via [[Tikhonov regularization]] and in the L2 norm sense and also corresponds to minimizing the bias and variance of our estimateestimator of the weights. Estimators with lower mean-square error predict better or generalize better when given unseen data.

Specifically, [[Tikhonov regularization]] algorithms produce a decision boundary that minimizes the average training-set error and constrains the model to not be excessively complicated or overfit the training data via a L2 norm of the weights term. The training and test-set errors can be measured without bias and in a fair way using accuracy, precision, Auc-Roc, precision-recall, and other metrics.