A convolutional Siamese network for developing similarity knowledge in the SelfBACK dataset.
Martin, Kyle; Wiratunga, Nirmalie; Sani, Sadiq; Massie, Stewart; Clos, Jérémie
Professor Nirmalie Wiratunga email@example.com
Doctor Stewart Massie firstname.lastname@example.org
Senior Research Fellow
Antonio A. Sanchez-Ruiz
The Siamese Neural Network (SNN) is a neural network architecture capable of learning similarity knowledge between cases in a case base by receiving pairs of cases and analysing the differences between their features to map them to a multi-dimensional feature space. This paper demonstrates the development of a Convolutional Siamese Network (CSN) for the purpose of case similarity knowledge generation on the SelfBACK dataset. We also demonstrate a CSN is capable of performing classification on the SelfBACK dataset to an accuracy which is comparable with a standard Convolutional Neural Network.
|Start Date||Jun 26, 2017|
|Publication Date||Dec 18, 2017|
|Publisher||CEUR Workshop Proceedings|
|Series Title||CEUR workshop proceedings|
|Institution Citation||MARTIN, K., WIRATUNGA, N., SANI, S., MASSIE, S. and CLOS, J. 2017. A convolutional Siamese network for developing similarity knowledge in the SelfBACK dataset. In Sanchez-Ruiz, A.A. and Kofod-Petersen, A. (eds.) Workshop proceedings of the 25th International conference on case-based reasoning (ICCBR 2017), 26-29 June 2017, Trondheim, Norway. CEUR workshop proceedings, 2028. Aachen: CEUR-WS [online], session 2: case-based reasoning and deep learning workshop (CBRDL-2017), pages 85-94. Available from: http://ceur-ws.org/Vol-2028/paper8.pdf|
|Keywords||Case based reasoning; Siamese neural networks; Categorisation; SelfBACK|
MARTIN 2017 A convolutional Siamese network
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