我只想知道神经网络是否可以用一类数据集进行训练。 我有一组数据，我想训练一个神经网络，在训练之后，我想给训练的神经网络提供一个新数据（用于测试），以检查它是否可以识别它是否与训练样本类似或不。
神经网络有可能吗？ 如果是，那将是有监督的学习还是无人监督。
我知道神经网络可以用于分类，如果有多个类但我以前没有见过单个类。 将非常感谢您对任何示例的良好解释和链接。 谢谢
I just want to know if a neural network can be trained with a single class of data set. I have a set of data that I want to train a neural network with. After training it, I want to give new data(for testing) to the trained neural network to check if it can recognize it as been similar to the training sample or not.
Is this possible with neural network? If yes, will that be a supervised learning or unsupervised.
I know neural networks can be used for classification if there are multiple classes but I have not seen with a single class before. A good explanation and link to any example will be much appreciated. Thanks
最满意答案
当然可以。 但在这种情况下，它只会识别你训练过的这一课程。 根据预期的输出，您可以测量与训练数据的相似性。
训练后，NN只是一个功能。 对于分类问题，您可以将其想象为一个函数，它将数据作为输入并返回一个整数，指示它属于哪个类。 话虽这么说，如果你只有一个类可以用整数值1表示，如果训练数据与那个类不相似，你会得到类似1.555的东西; 它不会告诉你它属于另一个类，因为你只引入了一个，但它肯定会给你一个关于它的相似性的暗示。
NN被认为是监督学习，因为在训练之前你必须提供输入和目标，即预期输出。
Of course it can be. But in this case it will only recognize this one class that you have trained it with. And depending on the expected output you can measure the similarity to the training data.
An NN, after training, is just a function. For classification problems you can imagine it as a function that takes data as input and returns an integer indicating to which class it belongs to. That being said, if you have only one class that can be represented by an integer value 1, and if training data is not similar to that class, you will get something like 1.555; It will not tel you that it belongs to another class, because you have introduced only one, but it will definitely give you a hint about its similarity.
NNs are considered to be supervised learning, because before training you have to provide both input and target, i. e. the expected output.
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