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→Relation to optimization
机器学习和数据挖掘虽然在使用方法上有些相似并且有很大的重叠，但是机器学习的重点是预测，基于从训练数据中学到的已知属性，而数据挖掘的重点则是发现数据中（以前）未知的属性（这是'''数据库中知识发现 Knowledge Discovery in Database, KDD'''的基本分析步骤），也就是说数据挖掘虽然使用了许多机器学习方法，但二者的目标不同; 另一方面，机器学习也使用数据挖掘方法作为“无监督学习”或作为提高学习者准确性的预处理步骤。这两个研究领域之间的混淆（这两个领域通常有各自单独的会议和单独的期刊，ECML PKDD是一个例外）来自他们工作的基本假设: 在机器学习中，算法性能通常是根据再现已知知识的能力来评估，而在知识发现和数据挖掘中，其关键任务是发现以前未知的知识，因此在对已知知识进行评价时，其他监督方法很容易超过未知（无监督）方法，而在典型的知识发现任务中，由于缺乏训练数据，无法使用有监督的学习算法。
机器学习和数据挖掘虽然在使用方法上有些相似并且有很大的重叠，但是机器学习的重点是预测，基于从训练数据中学到的已知属性，而数据挖掘的重点则是发现数据中（以前）未知的属性（这是'''数据库中知识发现 Knowledge Discovery in Database, KDD'''的基本分析步骤），也就是说数据挖掘虽然使用了许多机器学习方法，但二者的目标不同; 另一方面，机器学习也使用数据挖掘方法作为“无监督学习”或作为提高学习者准确性的预处理步骤。这两个研究领域之间的混淆（这两个领域通常有各自单独的会议和单独的期刊，ECML PKDD是一个例外）来自他们工作的基本假设: 在机器学习中，算法性能通常是根据再现已知知识的能力来评估，而在知识发现和数据挖掘中，其关键任务是发现以前未知的知识，因此在对已知知识进行评价时，其他监督方法很容易超过未知（无监督）方法，而在典型的知识发现任务中，由于缺乏训练数据，无法使用有监督的学习算法。
=== Relation to optimization ===
=== 与优化的关系 Relation to optimization ===
Machine learning also has intimate ties to [[Mathematical optimization|optimization]]: many learning problems are formulated as minimization of some [[loss function]] on a training set of examples. Loss functions express the discrepancy between the predictions of the model being trained and the actual problem instances (for example, in classification, one wants to assign a label to instances, and models are trained to correctly predict the pre-assigned labels of a set of examples). The difference between the two fields arises from the goal of generalization: while optimization algorithms can minimize the loss on a training set, machine learning is concerned with minimizing the loss on unseen samples.<ref>{{cite encyclopedia |last1=Le Roux |first1=Nicolas |first2=Yoshua |last2=Bengio |first3=Andrew |last3=Fitzgibbon |title=Improving First and Second-Order Methods by Modeling Uncertainty |encyclopedia=Optimization for Machine Learning |year=2012 |page=404 |editor1-last=Sra |editor1-first=Suvrit |editor2-first=Sebastian |editor2-last=Nowozin |editor3-first=Stephen J. |editor3-last=Wright |publisher=MIT Press|url=https://books.google.com/?id=JPQx7s2L1A8C&pg=PA403&dq="Improving+First+and+Second-Order+Methods+by+Modeling+Uncertainty|isbn=9780262016469 }}</ref>
Machine learning also has intimate ties to [[Mathematical optimization|optimization]]: many learning problems are formulated as minimization of some [[loss function]] on a training set of examples. Loss functions express the discrepancy between the predictions of the model being trained and the actual problem instances (for example, in classification, one wants to assign a label to instances, and models are trained to correctly predict the pre-assigned labels of a set of examples). The difference between the two fields arises from the goal of generalization: while optimization algorithms can minimize the loss on a training set, machine learning is concerned with minimizing the loss on unseen samples.<ref>{{cite encyclopedia |last1=Le Roux |first1=Nicolas |first2=Yoshua |last2=Bengio |first3=Andrew |last3=Fitzgibbon |title=Improving First and Second-Order Methods by Modeling Uncertainty |encyclopedia=Optimization for Machine Learning |year=2012 |page=404 |editor1-last=Sra |editor1-first=Suvrit |editor2-first=Sebastian |editor2-last=Nowozin |editor3-first=Stephen J. |editor3-last=Wright |publisher=MIT Press|url=https://books.google.com/?id=JPQx7s2L1A8C&pg=PA403&dq="Improving+First+and+Second-Order+Methods+by+Modeling+Uncertainty|isbn=9780262016469 }}</ref>
Machine learning also has intimate ties to optimization: many learning problems are formulated as minimization of some loss function on a training set of examples. Loss functions express the discrepancy between the predictions of the model being trained and the actual problem instances (for example, in classification, one wants to assign a label to instances, and models are trained to correctly predict the pre-assigned labels of a set of examples). The difference between the two fields arises from the goal of generalization: while optimization algorithms can minimize the loss on a training set, machine learning is concerned with minimizing the loss on unseen samples.
Machine learning also has intimate ties to optimization: many learning problems are formulated as minimization of some loss function on a training set of examples. Loss functions express the discrepancy between the predictions of the model being trained and the actual problem instances (for example, in classification, one wants to assign a label to instances, and models are trained to correctly predict the pre-assigned labels of a set of examples). The difference between the two fields arises from the goal of generalization: while optimization algorithms can minimize the loss on a training set, machine learning is concerned with minimizing the loss on unseen samples.
机器学习与优化也有着密切的联系: 许多学习问题被表述为最小化训练样本集上的某些损失函数。损失函数表示正在训练的模型的预测与实际问题实例之间的差异(例如，在分类中，人们希望为实例分配一个标签，而模型则被训练以正确预测一组实例的预先分配的标签)。这两个领域之间的差异源于泛化的目标: 优化算法可以最小化训练集上的损失，而机器学习关注于最小化未知样本上的损失。
机器学习与优化也有着密切的联系: 许多学习问题被表述为最小化训练样本集上的某些'''损失函数 Loss function'''。损失函数表示正在训练的模型预测结果与实际数据之间的差异（例如，在分类问题中，人们的目标是给一个未知的实例分配其对应标签，而模型经过训练学习到的是如何正确地为一组实例标记事先已知的标签）。这两个领域之间的差异源于泛化的目标: 优化算法可以最小化训练集上的损失，而机器学习关注于最小化未知样本上的损失。
=== Relation to statistics ===
=== Relation to statistics ===