问题描述
就人工智能和机器学习而言，有监督和无监督学习有什么区别?您能否通过示例提供基本，简单的说明?
由于您问了一个非常基本的问题，因此似乎值得指定机器学习本身.
机器学习是一类由数据驱动的算法，即与普通"算法不同的是，数据说明"了好答案".示例:一种用于图像中人脸检测的假想非机器学习算法将尝试定义人脸(皮肤圆形的彩色圆盘，期望眼睛的区域较暗，等等).机器学习算法将没有这样的编码定义，但会通过示例学习":您将显示几张面孔和非面孔的图像，并且一个好的算法最终将学习并能够预测是否看不见图像是一张脸.
这个特殊的面部检测示例是受监督的，这意味着您的示例必须带有标签，或明确指出哪些是面部，哪些不是. /p>
在无人监督算法中，您的示例没有被标记，即您什么也没说.当然，在这种情况下，算法本身无法发明"一张脸是什么，但是它可以尝试集群将数据分为不同的组，例如可以辨别出脸与风景有很大不同，而脸与马也有很大不同.
由于另一个答案提到了它(尽管以不正确的方式):存在中间"形式的监督，即半监督和主动学习.从技术上讲，这些是受监督的方法，其中有些智能"方法可以避免使用大量带标签的示例.在主动学习中，算法本身会决定您应该标记的东西(例如，可以很确定地识别风景和马匹，但是它可能会要求您确认大猩猩是否确实是一张脸的图片).在半监督学习中，有两种不同的算法，它们以带标签的示例开头，然后以彼此思考大量未标签数据的方式相互讲述".他们从这种讨论"中学习.
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?
Since you ask this very basic question, it looks like it's worth specifying what Machine Learning itself is.
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.
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.
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.
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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