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Deep Learning via Semi-Supervised Embedding
This video was recorded at 25th International Conference on Machine Learning (ICML), Helsinki 2008. 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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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
