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第497行： − 半监督学习位于非监督式学习(没有任何标记的训练数据)和监督式学习(完全标记的训练数据)之间。有些训练样本缺少训练标签，但许多机器学习研究人员发现，如果将未标记的数据与少量标记的数据结合使用，可以大大提高学习的准确性。+ 第506行：
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→Semi-supervised learning
==== Semi-supervised learning ====
==== 半监督学习 Semi-supervised learning ====
{{Main|Semi-supervised learning}}
{{Main|Semi-supervised learning}}
Semi-supervised learning falls between unsupervised learning (without any labeled training data) and supervised learning (with completely labeled training data). Some of the training examples are missing training labels, yet many machine-learning researchers have found that unlabeled data, when used in conjunction with a small amount of labeled data, can produce a considerable improvement in learning accuracy.
Semi-supervised learning falls between unsupervised learning (without any labeled training data) and supervised learning (with completely labeled training data). Some of the training examples are missing training labels, yet many machine-learning researchers have found that unlabeled data, when used in conjunction with a small amount of labeled data, can produce a considerable improvement in learning accuracy.
半监督学习介于无监督式学习（没有任何标记的训练数据）和有监督学习（完全标记的训练数据）之间。有些训练样本缺少训练标签，但许多机器学习研究人员发现，如果将未标记的数据与少量标记的数据结合使用，可以大大提高学习的准确性。
在弱监督式学习中，训练标签是噪声的、有限的或不精确的; 然而，这些标签往往更便宜，导致更大的有效训练集。
在弱监督式学习中，训练标签是噪声的、有限的或不精确的; 然而，这些标签往往更便宜，导致更大的有效训练集。
==== Reinforcement learning ====
==== Reinforcement learning ====