Stacked Autoencoders
From Ufldl
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- | The information of interest is contained within <math>a^{(n)}</math>, which is the activation of the deepest layer of hidden units. This vector gives us a representation of the input in terms of higher-order features. The stacked autoencoder can be used for classification problems by feeding a( | + | The information of interest is contained within <math>a^{(n)}</math>, which is the activation of the deepest layer of hidden units. This vector gives us a representation of the input in terms of higher-order features. |
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+ | The features from the stacked autoencoder can be used for classification problems by feeding <math>a(n)</math> to a softmax classifier. | ||
===Training=== | ===Training=== |