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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ones are motorcycles), then we could use this form of unlabeled data to
ones are motorcycles), then we could use this form of unlabeled data to
learn the features.  This setting---where each unlabeled example is drawn from the same
learn the features.  This setting---where each unlabeled example is drawn from the same
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distribution as your labeled examples---is sometimes called the '''semi-supervised'''
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distribution as your labeled examples---is sometimes called the semi-supervised  
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setting.  In practice, we rarely have this sort of unlabeled data (where would you
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setting.  In practice, we often do not have this sort of unlabeled data (where would you
get a database of images where every image is either a car or a motorcycle, but
get a database of images where every image is either a car or a motorcycle, but
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
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data, the self-taught learning setting is much more broadly applicable.
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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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