PAC Learning Framework
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A comprehensive introduction to the PAC learning framework and its extensions, starting from basic concepts such as generalization error, empirical error, and PAC learnability. Through specific examples, it analyzes sample complexity and algorithm performance, and discusses Bayesian error rate, noise, and the decomposition of estimation error and approximation error in agnostic PAC learning. It further introduces optimization strategies such as structural risk minimization and regularization methods, aiming to provide systematic guidance for understanding the theoretical basis and practical application of learning algorithms.