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== History and relationships to other fields ==

== 历史以及和其他领域的关系History and relationships to other fields ==

{{see also|Timeline of machine learning}}

{{see also|Timeline of machine learning}}

The term machine learning was coined in 1959 by Arthur Samuel, an American IBMer and pioneer in the field of computer gaming and artificial intelligence.  A representative book of the machine learning research during the 1960s was the Nilsson's book on Learning Machines, dealing mostly with machine learning for pattern classification. Interest related to pattern recognition continued into the 1970s, as described by Duda and Hart in 1973.  In 1981 a report was given on using teaching strategies so that a neural network learns to recognize 40 characters (26 letters, 10 digits, and 4 special symbols) from a computer terminal.   

The term machine learning was coined in 1959 by Arthur Samuel, an American IBMer and pioneer in the field of computer gaming and artificial intelligence.  A representative book of the machine learning research during the 1960s was the Nilsson's book on Learning Machines, dealing mostly with machine learning for pattern classification. Interest related to pattern recognition continued into the 1970s, as described by Duda and Hart in 1973.  In 1981 a report was given on using teaching strategies so that a neural network learns to recognize 40 characters (26 letters, 10 digits, and 4 special symbols) from a computer terminal.   

机器学习这个术语是1959年由美国 IBMer 创造的，他是计算机游戏和人工智能领域的先驱。20世纪60年代机器学习研究的一本代表性书籍是尼尔森的《学习机器》 ，主要是关于模式分类的机器学习。正如 Duda 和 Hart 在1973年所描述的那样，与模式识别相关的兴趣一直持续到20世纪70年代。1981年，一份关于使用教学策略使神经网络从计算机终端学习识别40个字符(26个字母、10个数字和4个特殊符号)的报告发表。

机器学习这个名词最早是1959年由美国 IBMer 提出的，他是计算机游戏和人工智能领域的先驱。20世纪60年代机器学习研究的一本代表性书籍是尼尔森的《学习机器 Learning Machines》，主要介绍了模式分类上的机器学习方法。正如 Duda 和 Hart 在1973年所描述的那样，研究人员对模式识别的研究兴趣一直持续到了20世纪70年代。1981年，一份关于利用学习策略使神经网络从计算机终端学习识别到了40个字符（26个字母、10个数字和4个特殊符号）的报告发表。

Tom M. Mitchell provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P,  improves with experience E."<ref name="Mitchell-1997">{{cite book

Tom M. Mitchell provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P,  improves with experience E."<ref name="Mitchell-1997">{{cite book

汤姆 · 米切尔对机器学习领域研究的算法提供了一个被广泛引用的、更为正式的定义: “据说计算机程序在某类任务 t 和性能测量 p 方面从经验 e 中学习，如果它在 t 任务中的性能，按 p 衡量，随着经验 e 的改进而得到改进的话。” ref name"Mitchell-1997"{ cite book

汤姆 · 米切尔 Tom M. Mitchell对机器学习领域研究的算法提供了一个被广泛引用的、更为正式的定义: “一个电脑程序要完成任务（T），如果电脑获取的关于T的经验（E）越多就表现（P）得越好，那么我们就可以说这个程序‘学习’了关于T的经验。” ref name"Mitchell-1997"{ cite book

|author=Mitchell, T.  

|author=Mitchell, T.  

|year=1997}}</ref> This definition of the tasks in which machine learning is concerned offers a fundamentally operational definition rather than defining the field in cognitive terms. This follows Alan Turing's proposal in his paper "Computing Machinery and Intelligence", in which the question "Can machines think?" is replaced with the question "Can machines do what we (as thinking entities) can do?".  

|year=1997}}</ref> This definition of the tasks in which machine learning is concerned offers a fundamentally operational definition rather than defining the field in cognitive terms. This follows Alan Turing's proposal in his paper "Computing Machinery and Intelligence", in which the question "Can machines think?" is replaced with the question "Can machines do what we (as thinking entities) can do?".  

这个关于机器学习任务的定义提供了一个基本的操作型定义，而不是用认知的术语来定义这个领域。此前，阿兰 · 图灵在他的论文《计算机器与智能》中提出了“机器能思考吗? ”取而代之的是“机器能做我们(作为思考实体)能做的事情吗? ”？".

这个关于机器学习任务的定义提供了一个基本的操作型定义，但并不是通过已知的术语来进行定义的。此前，阿兰 · 图灵 Alan Turing在他的论文《计算机器与智能 Computing Machinery and Intelligence》中提出了“机器能思考吗?”这个问题的替代问题为“机器能做我们(作为思考实体)能做的事情吗？”.

=== Relation to artificial intelligence ===

=== 与人工智能 Artificial intelligence的关系Relation to artificial intelligence ===

As a scientific endeavor, machine learning grew out of the quest for artificial intelligence. In the early days of AI as an [[Discipline (academia)|academic discipline]], some researchers were interested in having machines learn from data. They attempted to approach the problem with various symbolic methods, as well as what were then termed "[[neural network]]s"; these were mostly [[perceptron]]s and [[ADALINE|other models]] that were later found to be reinventions of the [[generalized linear model]]s of statistics.<ref>{{cite citeseerx |last1=Sarle |first1=Warren |title=Neural Networks and statistical models |citeseerx=10.1.1.27.699 |year=1994}}</ref> [[Probability theory|Probabilistic]] reasoning was also employed, especially in automated [[medical diagnosis]].<ref name="aima">{{cite AIMA|edition=2}}</ref>{{rp|488}}

As a scientific endeavor, machine learning grew out of the quest for artificial intelligence. In the early days of AI as an [[Discipline (academia)|academic discipline]], some researchers were interested in having machines learn from data. They attempted to approach the problem with various symbolic methods, as well as what were then termed "[[neural network]]s"; these were mostly [[perceptron]]s and [[ADALINE|other models]] that were later found to be reinventions of the [[generalized linear model]]s of statistics.<ref>{{cite citeseerx |last1=Sarle |first1=Warren |title=Neural Networks and statistical models |citeseerx=10.1.1.27.699 |year=1994}}</ref> [[Probability theory|Probabilistic]] reasoning was also employed, especially in automated [[medical diagnosis]].<ref name="aima">{{cite AIMA|edition=2}}</ref>{{rp|488}}

一些统计学家采用了机器学习的方法，形成了一个他们称之为统计学习的综合领域。

一些统计学家采用了机器学习的方法，形成了一个他们称之为统计学习的综合领域。

== {{anchor|Generalization}} Theory ==

== {{anchor|Generalization}} Theory ==