In this tutorial paper, we first define mean squared error, variance, covariance, and bias of both random variables and classification/predictor models. Then, we formulate the true and generalization errors of the model for both training and validation/test instances where we make use of the Stein's Unbiased Risk Estimator (SURE). We define overfitting, underfitting, and generalization using the obtained true and generalization errors. We introduce cross validation and two well-known examples which are $K$-fold and leave-one-out cross validations. We briefly introduce generalized cross validation and then move on to regularization where we use the SURE again. We work on both $\ell_2$ and $\ell_1$ norm regularizations. Then, we show that bootstrap aggregating (bagging) reduces the variance of estimation. Boosting, specifically AdaBoost, is introduced and it is explained as both an additive model and a maximum margin model, i.e., Support Vector Machine (SVM). The upper bound on the generalization error of boosting is also provided to show why boosting prevents from overfitting. As examples of regularization, the theory of ridge and lasso regressions, weight decay, noise injection to input/weights, and early stopping are explained. Random forest, dropout, histogram of oriented gradients, and single shot multi-box detector are explained as examples of bagging in machine learning and computer vision. Finally, boosting tree and SVM models are mentioned as examples of boosting.

翻译：在本教程中，我们首先定义随机变量及分类/预测模型的均方误差、方差、协方差和偏差。随后，我们利用Stein无偏风险估计（SURE）推导模型在训练集和验证集/测试集上的真实误差与泛化误差。通过所得的真实误差与泛化误差，我们定义过拟合、欠拟合和泛化能力。我们介绍交叉验证及其两个经典实例：K折交叉验证和留一交叉验证。简要介绍广义交叉验证后，我们转向正则化方法，并再次使用SURE进行分析。我们分别探讨ℓ2范数和ℓ1范数正则化。接着，我们证明Bootstrap聚合（Bagging）能降低估计的方差。我们介绍Boosting（特别是AdaBoost），并将其解释为加性模型和最大间隔模型（即支持向量机，SVM）。同时，我们给出了Boosting泛化误差的上界，以说明Boosting为何能防止过拟合。作为正则化的实例，我们详细解释了岭回归与套索回归、权重衰减、输入/权重噪声注入以及早停法。在机器学习和计算机视觉领域，我们将随机森林、Dropout、方向梯度直方图和单射多框检测器作为Bagging的实例进行说明。最后，我们提及提升树和SVM模型作为Boosting的实例。