Deep Evolution of Feature Representations for Handwritten Digit Recognition

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Title: Deep Evolution of Feature Representations for Handwritten Digit Recognition
Authors: Agapitos, Alexandros
O'Neill, Michael
Nicolau, Miguel
Fagan, David
Kattan, Ahmed
Curran, Kathleen M.
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Date: 28-May-2015
Abstract: A training protocol for learning deep neural networks, called greedy layer-wise training, is applied to the evolution of a hierarchical, feed-forward Genetic Programming based system for feature construction and object recognition. Results on a popular handwritten digit recognition benchmark clearly demonstrate that two layers of feature transformations improves generalisation compared to a single layer. In addition, we show that the proposed system outperforms several standard Genetic Programming systems, which are based on hand-designed features, and use different program representations and fitness functions.
Type of material: Conference Publication
Publisher: IEEE
Copyright (published version): 2015 IEEE
Keywords: Object recognition;Feature extraction;Genetic algorithms;Handwritten character recognition
DOI: 10.1109/CEC.2015.7257189
Language: en
Status of Item: Peer reviewed
Conference Details: 2015 IEEE Congress on Evolutionary Computation (CEC), Sendai, Japan, 25-28 May 2015
Appears in Collections:Business Research Collection
CASL Research Collection

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