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Neural Network Programming with Java by Fábio M. Soares, Alan M.F. Souza

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Some unsupervised learning algorithms

There are a multitude of unsupervised algorithms, not only for neural networks. Examples are K-means, expectation maximization, methods of moments, and so on. Such algorithms assume the entire dataset as the knowledge to be learned, so one common feature through all the learning algorithms is that they do not have an input–output relationship in the current dataset. However, one wishes to find a different meaning of these data, and that's the goal of any unsupervised learning algorithm.

Bringing this concept into the neural network context, let's take a look at an ANN and how it deals with data in an unsupervised organization.

The neural network output is considered to be an abstraction of the original data ...

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