Fine tuning of adaptive learning of deep belief network for misclassification and its knowledge acquisition Online publication date: Mon, 29-Jan-2018
by Shin Kamada; Takumi Ichimura
International Journal of Computational Intelligence Studies (IJCISTUDIES), Vol. 6, No. 4, 2017
Abstract: We have proposed an adaptive structure learning of deep belief network (DBN) that can determine the suitable number of hidden layers and hidden neurons of restricted Boltzmann machines (RBMs). The method shows high classification performance to the big data benchmark test. However, the method could not classify the unknown pattern correctly, since an input data with ambiguous patterns leads the classification to the wrong judgment. In such a case, a fine-tuning method that patches a part of network signal flow based on the knowledge will be a helpful method even in terms of both the improvement of classification capability and the reduction of computational cost by learning again. In this paper, network signal patterns which lead the given misclassified patterns were visualised for knowledge acquisition. By fine-tuning the trained network using the acquired knowledge, the classification capability can achieve great success.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computational Intelligence Studies (IJCISTUDIES):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook