A Convolutional Siamese Network for Developing Similarity Knowledge in the SelfBACK Dataset
نویسندگان
چکیده
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 di erences 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 classi cation on the SelfBACK dataset to an accuracy which is comparable with a standard Convolutional Neural Network.
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