English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

We developed an adaptive structure learning method of Restricted Boltzmann Machine (RBM) which can generate/annihilate neurons by self-organizing learning method according to input patterns. Moreover, the adaptive Deep Belief Network (DBN) in the assemble process of pre-trained RBM layer was developed. The proposed method presents to score a great success to the training data set for big data benchmark test such as CIFAR-10. However, the classification capability of the test data set, which are included unknown patterns, is high, but does not lead perfect correct solution. We investigated the wrong specified data and then some characteristic patterns were found. In this paper, the knowledge related to the patterns is embedded into the classification algorithm of trained DBN. As a result, the classification capability can achieve a great success (97.1% to unknown data set).

Fine tuning method by using knowledge acquisition from Deep Belief Network