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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。

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。

'''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。

 

 

==== 无监督学习 Unsupervised learning ====

==== 无监督学习 Unsupervised learning ====