Is regularization really ever used to reduce underfitting? In my experience, regularization is applied on a complex/sensitive model to reduce complexity/sensitvity, but never on a …

Dec 4, 2017 · When implementing a neural net (or other learning algorithm) often we want to regularize our parameters $\\theta_i$ via L2 regularization. We do this usually by adding a …

Dec 10, 2016 · Terms like "regularization of sequences" have been around in mathematics for a long time (certainly since the 1920s), which has a meaning fairly closely related to the …

On regularization for neural nets: When adjusting the weights while running the back-propagation algorithm, the regularization term is added to the cost function in the same manner as the …

By definition, a regularization parameter is any term that is in the optimized loss, but not the problem loss. Since the learning rate is acting like an extra quadratic term in the optimized …

Mar 19, 2023 · Regularization is a way of sacrificing the training loss value in order to improve some other facet of performance, a major example being to sacrifice the in-sample fit of a …

Aug 1, 2016 · Regularization helps in keeping these coefficients at lower values, hence, the curve is smooth. We now have less training points on the curve, more training error, but less test …

Jul 24, 2017 · In what circumstances should one consider using regularization methods (ridge, lasso or least angles regression) instead of OLS? In case this helps steer the discussion, my …

Jan 16, 2019 · I came across "Tikhonov regularization" and I have bare knowledge on it. It seems that it is a type of regularization that is important for deconvolution. Are there any good …

Mar 13, 2015 · A common way to reduce overfitting in a machine learning algorithm is to use a regularization term that penalizes large weights (L2) or non-sparse weights (L1) etc. How can …