Partial differential equation regularization for supervised machine learning
This article is an overview of supervised machine learning problems for regression and classification. Topics include: kernel methods, training by stochastic gradient descent, deep learning architecture, losses for classification, statistical learning theory, and dimension independent generalization bounds. Implicit regularization in deep learning examples are presented, including data augmentation, adversarial training, and additive noise. These methods are reframed as explicit gradient regularization.
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BIG-bench Machine LearningClassificationData AugmentationDeep LearningGeneral ClassificationGeneralization BoundsLearning TheoryregressionSimilar Papers 제목 키워드 기반
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