Deep Evolution of Feature Representations for Handwritten Digit Recognition

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{agapitos:cec2015,

• author = "Alexandros Agapitos and Michael O'Neill and Miguel Nicolau and David Fagan and Ahmed Kattan and Kathleen Curran",
• title = "Deep Evolution of Feature Representations for Handwritten Digit Recognition",
• booktitle = "Proceedings of 2015 IEEE Congress on Evolutionary Computation (CEC 2015)",
• editor = "Yadahiko Murata",
• pages = "2452--2459",
• year = "2015",
• address = "Sendai, Japan",
• publisher = "IEEE Press",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/CEC.2015.7257189",
• 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.",
• notes = "CEC2015",

}

Genetic Programming entries for Alexandros Agapitos Michael O'Neill Miguel Nicolau David Fagan Ahmed Kattan Kathleen Curran

Citations