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2019 | OriginalPaper | Chapter

A Shallow Learning - Reduced Data Approach for Image Classification

Authors : Kaleb E. Smith, Phillip Williams

Published in: Advances in Artificial Intelligence

Publisher: Springer International Publishing

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Abstract

Shepard Interpolation Neural Networks (SINN) lay a foundation addressing the flaws of deep algorithms, inspired by statistical interpolation techniques rather than biological brains it can be mathematically proven and the neuron interactions can be intuitively described. They also possess the ability to discriminate well with limited training data during the algorithm process. To enhance SINN from just regular vectorized images, we look to utilize hand designed and natural image features to help the SINN perform better on benchmark image classification data sets. We compare these input feature vectors using the SINN framework on three benchmark image classification test sets, showing comparable results to the state-of-the-art (SOTA) for a fraction of the computational and memory requirements due to SINN shallow learning ability.

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Title: A Shallow Learning - Reduced Data Approach for Image Classification
Authors: Kaleb E. Smith
Phillip Williams
Publisher: Springer International Publishing
Book: Advances in Artificial Intelligence
Print ISBN: 978-3-030-18304-2

Electronic ISBN: 978-3-030-18305-9

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-3-030-18305-9_28

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