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We want to learn
models with multiple layers of
non-
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linear
features.
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Perceptrons: Use a layer of hand-coded, non-adaptive
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features
followed by a layer of adaptive decision units.
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Needs
supervision signal for each training case.
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Only
one layer of adaptive weights.
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Back-propagation: Use multiple layers of adaptive
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features and
train by backpropagating error derivatives
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Needs
supervision signal for each training case.
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Learning
time scales poorly for deep networks.
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Support
Vector Machines: Use a very
large set of fixed
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features
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Needs
supervision signal for each training case.
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Does
not learn multiple layers of features
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