Deep learning via semi-supervised embedding

We show how nonlinear embedding algorithms popular for use with shallow semisupervised learning techniques such as kernel methods can be applied to deep multilayer 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.

• Type: Conference
• Year: 2008
• Where: ICML
• Authors: Frédéric Ratle, Jason Weston, Ronan Collobert
• Keywords: ICML 2008, Machine Learning, Nonlinear Embedding Algorithms, Shallow Semi-supervised Techniques, Shallow Semisupervised Learning