A Comparative Evaluation of Deep Belief Nets in Semi-supervised Learning
نویسنده
چکیده
In this report I studied the performance of deep belief nets (DBNs) on semisupervised 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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تاریخ انتشار 2008