Deep Evolution of Feature Representations for Handwritten Digit Recognition
|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.||Permanent link:||http://hdl.handle.net/10197/8274||Date:||28-May-2015||Online since:||2017-01-16T18:01:25Z||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||This item is made available under a Creative Commons License:||https://creativecommons.org/licenses/by-nc-nd/3.0/ie/|
|Appears in Collections:||Business Research Collection|
CASL Research Collection
Show full item record
Page view(s) 501,322
If you are a publisher or author and have copyright concerns for any item, please email firstname.lastname@example.org and the item will be withdrawn immediately. The author or person responsible for depositing the article will be contacted within one business day.