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#regularization_perspectives_on_support_vector_machines

Regularization perspectives on support vector machines

Within mathematical analysis, Regularization perspectives on support-vector machines provide a way of interpreting support-vector machines (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 estimator of the weights. Estimators with lower Mean squared error predict better or generalize better when given unseen data.

Thu 6th

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Regularization perspectives on support vector machines Support vector machine Machine learning Outline of machine learning Bayesian interpretation of kernel regularization Gradient boosting Stochastic gradient descent Adversarial machine learning Statistical learning theory Overfitting

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