Deep Learning via Semi-Supervised Embedding

author: Frederic Ratle, University of Lausanne
published: Aug. 5, 2008,   recorded: July 2008,   views: 380
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Description

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.

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