Stochastic Gradient, Machine Learning, AI, Gradient Descent.
pushing back on overfitting risk with high-dimensional data. The general idea is to trade off the “complexity” of the learned model (e.g., the magnitudes of weights of a linear prediction function) with its error on the data set. The goal of learning the simplest model with good explanatory power,