Deep Learning via Semi-Supervised Embedding
| Type of publication: | Book chapter |
| Citation: | Weston_SPRINGER_2012 |
| Booktitle: | In Neural Networks: Tricks of the Trade |
| Edition: | Second |
| Year: | 2012 |
| Publisher: | Springer |
| Abstract: | We show how nonlinear embedding algorithms popular for use with "shallow" semi-supervised learning techniques such as kernel methods can be easily applied to deep multi-layer architectures, either as a regularizer at the output layer, or on each layer of the architecture. This trick 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. |
| Keywords: | deep learning, embedding, semi-supervised learning |
| Projects: |
Idiap |
| Authors: | |
| Editors | |
| Added by: | [UNK] |
| Total mark: | 0 |
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