Self-Taught Learning

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(On the terminology of unsupervised feature learning)
 
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(perhaps with appropriate whitening or other pre-processing):
(perhaps with appropriate whitening or other pre-processing):
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[[File:STL_SparseAE.png]]
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[[File:STL_SparseAE.png|350px]]
Having trained the parameters <math>\textstyle W^{(1)}, b^{(1)}, W^{(2)}, b^{(2)}</math> of this model,
Having trained the parameters <math>\textstyle W^{(1)}, b^{(1)}, W^{(2)}, b^{(2)}</math> of this model,
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neural network:
neural network:
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[[File:STL_SparseAE_Features.png]]
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[[File:STL_SparseAE_Features.png|300px]]
This is just the sparse autoencoder that we previously had, with with the final
This is just the sparse autoencoder that we previously had, with with the final
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just missing its label?), and so in the context of learning features from unlabeled
just missing its label?), and so in the context of learning features from unlabeled
data, the self-taught learning setting is more broadly applicable.
data, the self-taught learning setting is more broadly applicable.
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{{STL}}
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{{Languages|自我学习|中文}}

Latest revision as of 13:26, 7 April 2013

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