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第417行：
第417行： − + − + 第429行：
第429行： − 机器学习算法的类型在它们的方法、它们输入和输出的数据的类型以及它们要解决的任务或问题的类型上都有所不同。+ − + 第441行：
第441行： − 支持向量机是一个监督式学习模型，它将数据划分为由线性边界分隔的区域。这里，线性边界将黑色圆圈和白色圆圈分开。]+ 第447行：
第447行： − 监督式学习算法建立一个包含输入和期望输出的数据集的数学模型。这些数据被称为训练数据，由一组训练示例组成。每个训练例子都有一个或多个输入和期望的输出，也称为监督信号。在数学模型中，每个训练样本由一个数组或向量表示，有时也称为特征向量，训练数据由一个矩阵表示。通过对目标函数的迭代优化，监督式学习算法学习一个可以用来预测与新输入相关的输出的函数。一个最优的函数将允许算法正确地确定输出的输入，而不是训练数据的一部分。一个随着时间推移提高输出或预测准确性的算法据说已经学会了执行这个任务。+ 第455行：
第455行： − 监督式学习算法的类型包括主动学习、分类和回归。当输出被限制在一个有限的值集内时使用分类算法，当输出在一个范围内可能有任何数值时使用回归算法。例如，对于过滤电子邮件的分类算法，输入将是一封收到的电子邮件，输出将是用于将电子邮件归档的文件夹的名称。+ 第463行：
第463行： − 相似性学习是监督式学习领域中与回归和分类密切相关的一个领域，但目标是通过使用相似性函数来衡量两个对象之间的相似程度，从实例中学习。它在排名、推荐系统、视觉身份跟踪、人脸验证和说话人确认等方面都有应用。+ − + 第475行：
第475行： − 非监督式学习算法只需要一组只包含输入的数据，然后在数据中找到结构，比如对数据点进行分组或聚类。因此，算法从未被标记、分类或分类的测试数据中学习。而不是响应反馈，非监督式学习算法识别数据中的共性，并根据每个新数据中是否存在这些共性而做出反应。非监督式学习的一个核心应用是统计学中的密度估计领域，比如寻找概率密度函数。尽管非监督式学习包含了其他领域，包括总结和解释数据特性。+ 第948行：
第948行： − −
→Approaches
除了性能界限，学习理论家们还研究学习的时间复杂性和可行性。在机器学习理论中，若一个计算被认为是可行的，当且仅当它可以在复杂度为多项式的时间范围内完成。关于时间复杂度的结果有两种。''正样结果 Negative results''表明，有些模式的函数可以在多项式时间复杂度内学习到。但同时''负样结果 Negative results''表明，某些模式并不能在多项式时间内学习到。
除了性能界限，学习理论家们还研究学习的时间复杂性和可行性。在机器学习理论中，若一个计算被认为是可行的，当且仅当它可以在复杂度为多项式的时间范围内完成。关于时间复杂度的结果有两种。''正样结果 Negative results''表明，有些模式的函数可以在多项式时间复杂度内学习到。但同时''负样结果 Negative results''表明，某些模式并不能在多项式时间内学习到。
== Approaches ==
== 方法 Approaches ==
=== Types of learning algorithms ===
=== 学习算法的分类 Types of learning algorithms ===
The types of machine learning algorithms differ in their approach, the type of data they input and output, and the type of task or problem that they are intended to solve.
The types of machine learning algorithms differ in their approach, the type of data they input and output, and the type of task or problem that they are intended to solve.
不同类型的机器学习算法的方法、输入和输出的数据类型以及它们要解决的任务或问题的类型都有所不同。
==== Supervised learning ====
==== 监督学习 Supervised learning ====
{{Main|Supervised learning}}
{{Main|Supervised learning}}
A [[support vector machine is a supervised learning model that divides the data into regions separated by a linear boundary. Here, the linear boundary divides the black circles from the white.]]
A [[support vector machine is a supervised learning model that divides the data into regions separated by a linear boundary. Here, the linear boundary divides the black circles from the white.]]
支持向量机是一个有监督学习模型，它将数据划分为由线性边界分隔的区域。在这里，有一个线性边界可以将黑色圆圈和白色圆圈分开。]
Supervised learning algorithms build a mathematical model of a set of data that contains both the inputs and the desired outputs.<ref>{{cite book |last1=Russell |first1=Stuart J. |last2=Norvig |first2=Peter |title=Artificial Intelligence: A Modern Approach |date=2010 |publisher=Prentice Hall |isbn=9780136042594 |edition=Third|title-link=Artificial Intelligence: A Modern Approach }}</ref> The data is known as [[training data]], and consists of a set of training examples. Each training example has one or more inputs and the desired output, also known as a supervisory signal. In the mathematical model, each training example is represented by an [[array data structure|array]] or vector, sometimes called a feature vector, and the training data is represented by a [[Matrix (mathematics)|matrix]]. Through iterative optimization of an [[Loss function|objective function]], supervised learning algorithms learn a function that can be used to predict the output associated with new inputs.<ref>{{cite book |last1=Mohri |first1=Mehryar |last2=Rostamizadeh |first2=Afshin |last3=Talwalkar |first3=Ameet |title=Foundations of Machine Learning |date=2012 |publisher=The MIT Press |isbn=9780262018258}}</ref> An optimal function will allow the algorithm to correctly determine the output for inputs that were not a part of the training data. An algorithm that improves the accuracy of its outputs or predictions over time is said to have learned to perform that task.<ref name="Mitchell-1997" />
Supervised learning algorithms build a mathematical model of a set of data that contains both the inputs and the desired outputs.<ref>{{cite book |last1=Russell |first1=Stuart J. |last2=Norvig |first2=Peter |title=Artificial Intelligence: A Modern Approach |date=2010 |publisher=Prentice Hall |isbn=9780136042594 |edition=Third|title-link=Artificial Intelligence: A Modern Approach }}</ref> The data is known as [[training data]], and consists of a set of training examples. Each training example has one or more inputs and the desired output, also known as a supervisory signal. In the mathematical model, each training example is represented by an [[array data structure|array]] or vector, sometimes called a feature vector, and the training data is represented by a [[Matrix (mathematics)|matrix]]. Through iterative optimization of an [[Loss function|objective function]], supervised learning algorithms learn a function that can be used to predict the output associated with new inputs.<ref>{{cite book |last1=Mohri |first1=Mehryar |last2=Rostamizadeh |first2=Afshin |last3=Talwalkar |first3=Ameet |title=Foundations of Machine Learning |date=2012 |publisher=The MIT Press |isbn=9780262018258}}</ref> An optimal function will allow the algorithm to correctly determine the output for inputs that were not a part of the training data. An algorithm that improves the accuracy of its outputs or predictions over time is said to have learned to perform that task.<ref name="Mitchell-1997" />
Supervised learning algorithms build a mathematical model of a set of data that contains both the inputs and the desired outputs. The data is known as training data, and consists of a set of training examples. Each training example has one or more inputs and the desired output, also known as a supervisory signal. In the mathematical model, each training example is represented by an array or vector, sometimes called a feature vector, and the training data is represented by a matrix. Through iterative optimization of an objective function, supervised learning algorithms learn a function that can be used to predict the output associated with new inputs. An optimal function will allow the algorithm to correctly determine the output for inputs that were not a part of the training data. An algorithm that improves the accuracy of its outputs or predictions over time is said to have learned to perform that task.
Supervised learning algorithms build a mathematical model of a set of data that contains both the inputs and the desired outputs. The data is known as training data, and consists of a set of training examples. Each training example has one or more inputs and the desired output, also known as a supervisory signal. In the mathematical model, each training example is represented by an array or vector, sometimes called a feature vector, and the training data is represented by a matrix. Through iterative optimization of an objective function, supervised learning algorithms learn a function that can be used to predict the output associated with new inputs. An optimal function will allow the algorithm to correctly determine the output for inputs that were not a part of the training data. An algorithm that improves the accuracy of its outputs or predictions over time is said to have learned to perform that task.
有监督学习算法会建立一个包含输入和期望输出的数据集上的的数学模型。这些数据被称为训练数据，由一组组训练样本组成。每个训练样本都有一个或多个输入和期望的输出，也称为监督信号。在数学模型中，每个训练样本由一个数组或向量表示，有时也称为'''特征向量 Feature vector'''，训练数据由一个矩阵表示。通过对目标函数的迭代优化，监督式学习算法可以学习到一个用来预测与新输入相关的输出的函数。一个达到最优的目标函数可以实现算法对未知输入的输出结果有正确的预判，这种正确的预判并不仅限于训练数据上（即模型具有良好的泛化能力）。随着时间的推移，提高输出或预测精度的算法被称为已学会执行该任务。
Types of supervised learning algorithms include Active learning , classification and regression. Classification algorithms are used when the outputs are restricted to a limited set of values, and regression algorithms are used when the outputs may have any numerical value within a range. As an example, for a classification algorithm that filters emails, the input would be an incoming email, and the output would be the name of the folder in which to file the email.
Types of supervised learning algorithms include Active learning , classification and regression. Classification algorithms are used when the outputs are restricted to a limited set of values, and regression algorithms are used when the outputs may have any numerical value within a range. As an example, for a classification algorithm that filters emails, the input would be an incoming email, and the output would be the name of the folder in which to file the email.
监督式学习算法的类型包括'''主动学习 Active learning'''、'''分类 Classification'''和'''回归 Regression'''。当输出被限制在一个有限的值集内时使用分类算法，当输出在一个范围内可能有任何数值时使用回归算法。例如，对于过滤电子邮件的分类算法，输入将是一封收到的电子邮件，输出将是用于将电子邮件归档的文件夹的名称。
Similarity learning is an area of supervised machine learning closely related to regression and classification, but the goal is to learn from examples using a similarity function that measures how similar or related two objects are. It has applications in ranking, recommendation systems, visual identity tracking, face verification, and speaker verification.
Similarity learning is an area of supervised machine learning closely related to regression and classification, but the goal is to learn from examples using a similarity function that measures how similar or related two objects are. It has applications in ranking, recommendation systems, visual identity tracking, face verification, and speaker verification.
'''相似性学习 Similarity learning'''是监督学习领域中与回归和分类密切相关的一个领域，但其目标是从实例中学习如何通过使用相似性函数来衡量两个对象之间的相似程度。它在排名、推荐系统、视觉身份跟踪、人脸验证和语者验证 Speaker verification等方面都有应用。
==== Unsupervised learning ====
==== 无监督学习 Unsupervised learning ====
{{Main|Unsupervised learning}}{{See also|Cluster analysis}}
{{Main|Unsupervised learning}}{{See also|Cluster analysis}}
Unsupervised learning algorithms take a set of data that contains only inputs, and find structure in the data, like grouping or clustering of data points. The algorithms, therefore, learn from test data that has not been labeled, classified or categorized. Instead of responding to feedback, unsupervised learning algorithms identify commonalities in the data and react based on the presence or absence of such commonalities in each new piece of data. A central application of unsupervised learning is in the field of density estimation in statistics, such as finding the probability density function. Though unsupervised learning encompasses other domains involving summarizing and explaining data features.
Unsupervised learning algorithms take a set of data that contains only inputs, and find structure in the data, like grouping or clustering of data points. The algorithms, therefore, learn from test data that has not been labeled, classified or categorized. Instead of responding to feedback, unsupervised learning algorithms identify commonalities in the data and react based on the presence or absence of such commonalities in each new piece of data. A central application of unsupervised learning is in the field of density estimation in statistics, such as finding the probability density function. Though unsupervised learning encompasses other domains involving summarizing and explaining data features.
无监督式学习算法只需要一组只包含输入的数据，然后在数据中找到结构，比如对数据点进行分组或聚类。因此，算法从未被标记、分类或分类的测试数据中学习。而不是响应反馈，非监督式学习算法识别数据中的共性，并根据每个新数据中是否存在这些共性而做出反应。非监督式学习的一个核心应用是统计学中的密度估计领域，比如寻找概率密度函数。尽管非监督式学习包含了其他领域，包括总结和解释数据特性。
联邦学习是一种新的培训机器学习模型的方法，它分散了培训过程，允许用户通过不需要将他们的数据发送到一个集中的服务器来维护他们的隐私。通过将培训过程分散到许多设备上，这也提高了效率。例如，谷歌董事会使用联邦机器学习来训练用户手机上的搜索查询预测模型，而不必将个人搜索发送回谷歌。
联邦学习是一种新的培训机器学习模型的方法，它分散了培训过程，允许用户通过不需要将他们的数据发送到一个集中的服务器来维护他们的隐私。通过将培训过程分散到许多设备上，这也提高了效率。例如，谷歌董事会使用联邦机器学习来训练用户手机上的搜索查询预测模型，而不必将个人搜索发送回谷歌。
== Applications ==
== Applications ==