Learning polynomial neural networks of a near-optimal connectivity for detecting abnormal patterns in biometric data
Abstract
Existing Machine Learning (ML) approaches known from the literature require the user to set and experimentally adjust parameters of a decision model to achieve the best result. When artificial neural networks (ANNs) are employed, a typical problem is setting of a proper network structure and learning parameters that are required to minimise possible overfitting. We propose a new evolutionary strategy of learning an ANN structure of a near-optimal connectivity from the given data and show that such structures are less prone to overfitting. The resultant ANN consists of a reasonably small number of neurons that are concisely described by a set of short-term polynomial functions of variables that make a distinct contribution to the output. The proposed technique has been tested on the ML benchmarks and the results showed that the performance is comparable with that obtained by the conventional ML methods that require ad hoc tuning.Citation
Nyah N, Jakaite L, Schetinin V, Sant P, Aggoun A (2016) 'Learning polynomial neural networks of a near-optimal connectivity for detecting abnormal patterns in biometric data', SAI Computing Conference - London, Institute of Electrical and Electronics Engineers Inc..Additional Links
https://ieeexplore.ieee.org/document/7556014Type
Conference papers, meetings and proceedingsLanguage
enISBN
9781467384605ae974a485f413a2113503eed53cd6c53
10.1109/SAI.2016.7556014