Deep Learning via Semi-Supervised Embedding
published: Aug. 5, 2008, recorded: July 2008, views: 346
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We show how nonlinear embedding algorithms popular for use with shallow semi-supervised learning techniques such as kernel methods can be applied to deep multi-layer architectures, either as a regularizer at the output layer, or on each layer of the architecture. This provides a simple alternative to existing approaches to deep learning whilst yielding competitive error rates compared to those methods, and existing shallow semi-supervised techniques.
Download slides: icml08_ratle_dls_01.pdf (1.5 MB)
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