Abstract

Deep learning is one of the most recent development form of Artificial Neural Network (ANN) in machine learning. Deep Neural Network (DNN) algorithm is usually used in image and speech recognition applications. As the development of Artificial Neural Network, very possible there are so many hidden layers in Deep Neural Network. In DNN, the output of each node is a quadratic function of its inputs. The DNN training process is very difficult. In this paper, we try to optimizing the training process by slightly construct of the deep architecture and combines several existing algorithms. Output's error of each unit in the previous layer will be calculated. The weight of the unit with the smallest error will be maintained in the next iteration. This paper uses MNIST handwriting images as its data training and data test. After doing some tests, it can be concluded that the optimization by selecting any output in each hidden layer, the DNN training process will be faster approximately 8%.

Original languageEnglish
Title of host publication2015 IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems, IEEE-CYBER 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1028-1032
Number of pages5
ISBN (Electronic)9781479987290
DOIs
Publication statusPublished - 2 Oct 2015
Event5th Annual IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems, IEEE-CYBER 2015 - Shenyang, China
Duration: 9 Jun 201512 Jun 2015

Publication series

Name2015 IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems, IEEE-CYBER 2015

Conference

Conference5th Annual IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems, IEEE-CYBER 2015
Country/TerritoryChina
CityShenyang
Period9/06/1512/06/15

Keywords

  • deep neural network
  • image and voice recognition
  • machine learning

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