A convolutional Siamese network for developing similarity knowledge in the SelfBACK dataset.
MetadataShow full item record
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.) Proceedings of the 25th International case-based reasoning conference (ICCBR 2017): case-based reasoning and deep learning workshop (CBRDL 2017), 26-28 June 2017, Trondheim, Norway. Trondheim: ICCBR [online], pages 85-94. Available from: http://ceur-ws.org/Vol-2028/paper8.pdf
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.