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A convolutional Siamese network for developing similarity knowledge in the SelfBACK dataset.

Martin, Kyle; Wiratunga, Nirmalie; Sani, Sadiq; Massie, Stewart; Clos, Jérémie

Authors

Kyle Martin

Sadiq Sani

Jérémie Clos



Contributors

Antonio A. Sanchez-Ruiz
Editor

Anders Kofod-Petersen
Editor

Abstract

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
Print ISSN 1613-0073
Publisher CEUR Workshop Proceedings
Pages 85-94
Series Title CEUR workshop proceedings
Series Number 2028
Series ISSN 1613-0073
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
Publisher URL http://ceur-ws.org/Vol-2028/paper8.pdf

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