Deep Evolution of Feature Representations for Handwritten Digit Recognition
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gp-bibliography.bib Revision:1.8081
- @InProceedings{agapitos:cec2015,
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author = "Alexandros Agapitos and Michael O'Neill and
Miguel Nicolau and David Fagan and Ahmed Kattan and
Kathleen Curran",
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title = "Deep Evolution of Feature Representations for
Handwritten Digit Recognition",
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booktitle = "Proceedings of 2015 IEEE Congress on Evolutionary
Computation (CEC 2015)",
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editor = "Yadahiko Murata",
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pages = "2452--2459",
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year = "2015",
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address = "Sendai, Japan",
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publisher = "IEEE Press",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/CEC.2015.7257189",
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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.",
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notes = "CEC2015",
- }
Genetic Programming entries for
Alexandros Agapitos
Michael O'Neill
Miguel Nicolau
David Fagan
Ahmed Kattan
Kathleen Curran
Citations