A Comparative Evaluation of Deep Belief Nets in |
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| Abstract | |
| In this report I studied the performance of deep belief nets (DBNs) on semi-supervised learning problems, in which only a small proportion of data are labeled. First the performance between DBNs and support vector machines (SVMs) are compared to investigate the advantage of deep models over shallow ones. I also explored the use of DBNs as pre-training for SVMs and feed-forward nets (FFNs). The experimental results show that DBN is able to yield state-of-art modeling power in semi-supervised learning. | |
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