File Classification Based on Spiking Neural Networks
- URL: http://arxiv.org/abs/2004.03953v1
- Date: Wed, 8 Apr 2020 11:50:29 GMT
- Title: File Classification Based on Spiking Neural Networks
- Authors: Ana Stanojevic, Giovanni Cherubini, Timoleon Moraitis, Abu Sebastian
- Abstract summary: We propose a system for file classification in large data sets based on spiking neural networks (SNNs)
The proposed system may represent a valid alternative to classical machine learning algorithms for inference tasks.
- Score: 0.5065947993017157
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this paper, we propose a system for file classification in large data sets
based on spiking neural networks (SNNs). File information contained in
key-value metadata pairs is mapped by a novel correlative temporal encoding
scheme to spike patterns that are input to an SNN. The correlation between
input spike patterns is determined by a file similarity measure. Unsupervised
training of such networks using spike-timing-dependent plasticity (STDP) is
addressed first. Then, supervised SNN training is considered by backpropagation
of an error signal that is obtained by comparing the spike pattern at the
output neurons with a target pattern representing the desired class. The
classification accuracy is measured for various publicly available data sets
with tens of thousands of elements, and compared with other learning
algorithms, including logistic regression and support vector machines.
Simulation results indicate that the proposed SNN-based system using memristive
synapses may represent a valid alternative to classical machine learning
algorithms for inference tasks, especially in environments with asynchronous
ingest of input data and limited resources.
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