Deep Evolution of Feature Representations for Handwritten Digit Recognition
|Title:||Deep Evolution of Feature Representations for Handwritten Digit Recognition||Authors:||Agapitos, Alexandros
Curran, Kathleen M.
|Permanent link:||http://hdl.handle.net/10197/8274||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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