CLASSIFYING DATA ITEMS USING REGULARISED SIMILARITY VALUES

A computer-implemented training method includes receiving a plurality of labelled training data items; generating a training data vector from each training data item; and determining prototype vector representative of training data items. A maximum similarity value between the training data vectors and the prototype vector is identified and stored with the prototype vector. A computer-implemented inference method includes generating an input vector from an input data item; calculating a first similarity value between the input vector and the prototype vector; and calculating a second similarity value based on the first similarity value and the maximum similarity value. The method then determines that the input data item is a member of the class of data items based on the second similarity value. The methods provide lightweight classifiers that can be rapidly trained by non-expert end users for applications such as object detection and cybersecurity risk detection.