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Slow subspace learning from stationary processes
Published on Feb 25, 20073125 Views
The talk presents a method of unsupervised learning from stationary, vector-valued processes. The method selects a subspace on the basis of an objective which can be used to bound the expected classif
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Chapter list
BOUNDS FOR LINEAR MTL00:02
ingredients of linear MTL00:34
objective04:10
error bound05:18
Rademacher complexity09:37
Hölders inequality11:40
theorem13:28
multi-task subspace learning23:39
error bound30:15
theorem33:40
Hölders inequality35:30