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.
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Apolloni, B., Shehhi, A.A., Damiani, E. (2021). The Simplification Conspiracy. In: Esposito, A., Faundez-Zanuy, M., Morabito, F., Pasero, E. (eds) Progresses in Artificial Intelligence and Neural Systems. Smart Innovation, Systems and Technologies, vol 184. Springer, Singapore. https://doi.org/10.1007/978-981-15-5093-5_2
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