We study in a quantitative way the efficacy of a social intelligence scheme that is an extension of Extreme Learning Machine paradigm. The key question we investigate is whether and how a collection of elementary learning parcels can replace a single algorithm that is well suited to learn a relatively complex function. Per se, the question is definitely not new, as it can be met in various fields ranging from social networks to bio-informatics. We use a well known benchmark as a touchstone to contribute its answer with both theoretical and numerical considerations.
The Simplification Conspiracy / B. Apolloni, A.A. Shehhi, E. Damiani (SMART INNOVATION, SYSTEMS AND TECHNOLOGIES). - In: Progresses in Artificial Intelligence and Neural Systems / [a cura di] A. Esposito, M. Faundez-Zanuy, F.C. Morabito, E. Pasero. - [s.l] : Springer, 2021. - ISBN 9789811550928. - pp. 11-23 [10.1007/978-981-15-5093-5_2]
The Simplification Conspiracy
B. Apolloni;E. Damiani
2021
Abstract
We study in a quantitative way the efficacy of a social intelligence scheme that is an extension of Extreme Learning Machine paradigm. The key question we investigate is whether and how a collection of elementary learning parcels can replace a single algorithm that is well suited to learn a relatively complex function. Per se, the question is definitely not new, as it can be met in various fields ranging from social networks to bio-informatics. We use a well known benchmark as a touchstone to contribute its answer with both theoretical and numerical considerations.File | Dimensione | Formato | |
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