A convolutional Siamese network for developing similarity knowledge in the SelfBACK dataset.
Professor Nirmalie Wiratunga email@example.com
Dr Stewart Massie firstname.lastname@example.org
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.
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
|Conference Name||25th International conference on case-based reasoning (ICCBR 2017)|
|Conference Location||Trondheim, Norway|
|Start Date||Jun 26, 2017|
|End Date||Jun 29, 2017|
|Acceptance Date||May 25, 2017|
|Online Publication Date||Jun 26, 2017|
|Publication Date||Dec 18, 2017|
|Deposit Date||Sep 4, 2017|
|Publicly Available Date||Sep 4, 2017|
|Publisher||CEUR Workshop Proceedings|
|Series Title||CEUR workshop proceedings|
|Keywords||Case based reasoning; Siamese neural networks; Categorisation; SelfBACK|
MARTIN 2017 A convolutional Siamese network
Publisher Licence URL
You might also like
Reasoning with counterfactual explanations for code vulnerability detection and correction.
Memory efficient federated deep learning for intrusion detection in IoT networks.
Improving kNN for human activity recognition with privileged learning using translation models.