This book covers the field of machine learning, which is the study of algorithms that allow computer programs to automatically improve through experience. The book is intended to support upper level undergraduate and introductory level graduate courses in machine learning.
作者简介
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Tom M. Mitchell，卡内基梅隆大学的教授，讲授机器学习等多门课程；美国人工智能协会（AAAL）的主席；美国 Machine Learning 杂志、国际机器学习年度会议（ICML）的创始人。
The inductive learning hypothesis. Any hypothesis found to approximate the target function well over a sufficiently large set of training examples will also approximate the target function well over other unobserved examples (查看原文)
We shall see that most current theory of machine learning rests on the crucial assumption that the distribution of training examples is identical to the distribution of test examples.
Concept learning. Inferring a boolean-valued function from training examples of its input and output. (查看原文)
讲PAC的7.2章节里 英文版P207原文 This definition implicitly assumes that the learner's hypothesis space H contains a hypothesis with arbitrarily small error for every target concept in C. 本来是想表达 虽未明讲，但该定义其实做了一个假定，即对于C中每个目标概...
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