有监督学习和无监督学习有什么区别? [英] What is the difference between supervised learning and unsupervised learning?

问题描述

在人工智能和机器学习方面，有监督学习和无监督学习有什么区别?你能用一个例子提供一个基本的、简单的解释吗?

In terms of artificial intelligence and machine learning, what is the difference between supervised and unsupervised learning? Can you provide a basic, easy explanation with an example?

推荐答案

既然你问了这个非常基本的问题，那么看起来值得说明机器学习本身是什么.

Since you ask this very basic question, it looks like it's worth specifying what Machine Learning itself is.

机器学习是一类数据驱动的算法，即与普通"算法不同的是，数据告诉"了好的答案"是什么.示例:用于图像中人脸检测的假设非机器学习算法将尝试定义人脸是什么(圆形皮肤颜色的圆盘，在您期望眼睛的地方有暗区等).机器学习算法不会有这样的编码定义，但会通过例子学习":你会展示几张人脸和非人脸的图像，一个好的算法最终会学习并能够预测一个看不见的图片是一张脸.

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.

这个人脸检测的特殊示例是监督，这意味着您的示例必须标记，或者明确说明哪些是人脸，哪些不是.

This particular example of face detection is supervised, which means that your examples must be labeled, or explicitly say which ones are faces and which ones aren't.

在无监督算法中，您的示例没有标记，即您什么也不说.当然，在这种情况下，算法本身无法发明"人脸是什么，但它可以尝试集群 将数据分成不同的组，例如它可以区分人脸与山水的区别，山水与马的区别很大.

In an unsupervised algorithm your examples are not labeled, i.e. you don't say anything. Of course, in such a case the algorithm itself cannot "invent" what a face is, but it can try to cluster the data into different groups, e.g. it can distinguish that faces are very different from landscapes, which are very different from horses.

由于另一个答案提到了它(尽管以错误的方式):存在中间"形式的监督，即半监督和主动学习.从技术上讲，这些是有监督的方法，其中有一些智能"方法可以避免大量标记示例.在主动学习中，算法本身决定你应该标记哪个东西(例如，它可以非常确定地景和马，但它可能会要求你确认大猩猩是否确实是一张脸的照片).在半监督学习中，有两种不同的算法，它们从标记的示例开始，然后告诉"彼此他们对大量未标记数据的看法.他们从这次讨论"中学到了东西.

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.

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