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The Shallow Neural Network (SNN) is one of the most basic forms of machine learning models. It has a high theoretical representational capacity, but this is often not realized in practice, in part due to overfitting. This thesis proposes a new regularization method and compares it against existing techniques across a range of tasks using SNNs. The proposed method, norm-preserving regularization (N
