在人工智能和机器学习方面,监督学习和无监督学习的区别是什么? 你能举个例子简单地解释一下吗?


当前回答

我一直认为无监督学习和有监督学习之间的区别是随意的,有点令人困惑。这两种情况之间没有真正的区别,相反,在一系列情况下,算法可以或多或少地“监督”。半监督学习的存在是界限模糊的一个明显例子。

我倾向于认为监督是对算法提供关于应该首选哪些解决方案的反馈。对于传统的监督设置,比如垃圾邮件检测,你告诉算法“不要在训练集上犯任何错误”;对于传统的无监督设置,比如聚类,你告诉算法“彼此接近的点应该在同一个聚类中”。很巧的是,第一种反馈形式比后者更具体。

简而言之,当有人说“有监督”时,想想分类,当他们说“无监督”时,想想聚类,尽量不要过于担心除此之外的问题。

其他回答

在监督学习中,我们知道输入和输出应该是什么。例如,给定一组汽车。我们得找出哪些是红的,哪些是蓝的。

然而,无监督学习是指我们必须在很少或没有任何关于输出应该如何的想法的情况下找到答案。例如,学习者可以建立一个模型,根据面部模式和单词(如“你在笑什么?”)的相关性来检测人们何时在微笑。

既然你问了这个非常基本的问题,似乎有必要详细说明机器学习本身是什么。

Machine Learning is a class of algorithms which is data-driven, i.e. unlike "normal" algorithms it is the data that "tells" what the "good answer" is. Example: a hypothetical non-machine learning algorithm for face detection in images would try to define what a face is (round skin-like-colored disk, with dark area where you expect the eyes etc). A machine learning algorithm would not have such coded definition, but would "learn-by-examples": you'll show several images of faces and not-faces and a good algorithm will eventually learn and be able to predict whether or not an unseen image is a face.

这个特殊的人脸检测的例子是有监督的,这意味着你的例子必须被标记,或者明确地说哪些是人脸,哪些不是。

在无监督算法中,你的例子没有标记,也就是说你什么都不说。当然,在这种情况下,算法本身不能“发明”人脸是什么,但它可以尝试将数据聚类到不同的组中,例如,它可以区分人脸与风景非常不同,而风景与马非常不同。

Since another answer mentions it (though, in an incorrect way): there are "intermediate" forms of supervision, i.e. semi-supervised and active learning. Technically, these are supervised methods in which there is some "smart" way to avoid a large number of labeled examples. In active learning, the algorithm itself decides which thing you should label (e.g. it can be pretty sure about a landscape and a horse, but it might ask you to confirm if a gorilla is indeed the picture of a face). In semi-supervised learning, there are two different algorithms which start with the labeled examples, and then "tell" each other the way they think about some large number of unlabeled data. From this "discussion" they learn.

机器学习是一个让机器模仿人类行为的领域。

你训练机器就像训练婴儿一样。人类学习、识别特征、识别模式并训练自己的方式,就像你通过输入各种特征的数据来训练机器一样。机器算法识别数据中的模式,并将其分类到特定的类别。

机器学习大致分为两类,有监督学习和无监督学习。

监督学习是一个概念,你有相应的目标值(输出)的输入向量/数据。另一方面,无监督学习的概念是只有输入向量/数据,没有任何相应的目标值。

监督学习的一个例子是手写数字识别,其中有对应数字[0-9]的数字图像,而非监督学习的一个例子是根据购买行为对客户进行分组。

监督学习基本上是一种技术,其中机器学习的训练数据已经被标记,假设是一个简单的偶数分类器,在训练过程中你已经对数据进行了分类。因此它使用“LABELLED”数据。

相反,无监督学习是一种机器自己标记数据的技术。或者你可以说这是机器从头开始自己学习的情况。

In simple words.. :) It's my understanding, feel free to correct. Supervised learning is, we know what we are predicting on the basis of provided data. So we have a column in the dataset which needs to be predicated. Unsupervised learning is, we try to extract meaning out of the provided dataset. We don't have clarity on what to be predicted. So question is why we do this?.. :) Answer is - the outcome of Unsupervised learning is groups/clusters(similar data together). So if we receive any new data then we associate that with the identified cluster/group and understand it's features.

我希望它能帮助你。