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Handwritten Digits Recognition using Ensemble Neural Networks and Ensemble Decision Tree

Larasati, Retno and KeungLam, Dr Hak (2017) Handwritten Digits Recognition using Ensemble Neural Networks and Ensemble Decision Tree. In: Proceedings of 2017 International Conference on Smart Cities, Automation & Intelligent Computing Systems (ICON-SONICS 2017), 08 November 207, Yogyakarta.

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Abstract

Handwriting recognition is widely used, and the using of neural network as a method to do is quite common. In this project, neural networks ensembles combined with another classifier are train and test in solving handwritten digit recognition problems, using USPS and MNIST database. The new proposed algorithm, ensemble neural networks that combined with ensemble decision tree (ENNEDT), performed better than single neural network and ensemble neural network. ENNEDT reached 84% accuracy from classifying USPS dataset. Matlab program implemented the training and testing functions to the handwritten digit recognising system.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: component; Ensemble Neural Network; Handwriting Recognition
Subjects: T Technology > T Technology (General) > T55 Industrial engineering. Management engineering > T59.5 Automation
T Technology > T Technology (General) > T55 Industrial engineering. Management engineering > T59.7-59.77 Human engineering in industry. Man-machine systems
Divisions: Universitas Multimedia Nusantara
Depositing User: mr admin umn
Date Deposited: 03 Mar 2018 10:28
Last Modified: 25 Apr 2018 10:39
URI: http://kc.umn.ac.id/id/eprint/2789

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