In this paper, we investigate the problem of music classification when training data is distributed throughout a network of interconnected agents (e.g. computers, or mobile devices), and it is available in a sequential stream. Under the considered setting, the task is for all the nodes, after receiving any new chunk of training data, to agree on a single classifier in a decentralized fashion, without reliance on a master node. In particular, in this paper we propose a fully decentralized, sequential learning algorithm for a class of neural networks known as Random Vector Functional-Link nets. The proposed algorithm does not require the presence of a single coordinating agent, and it is formulated exclusively in term of local exchanges between neighboring nodes, thus making it useful in a wide range of realistic situations. Experimental simulations on four music classification benchmarks show that the algorithm has comparable performance with respect to a centralized solution, where a single agent collects all the local data from every node and subsequently updates the model.

Distributed music classification using random vector functional-link nets / Scardapane, Simone; Fierimonte, R.; Wang, D.; Panella, Massimo; Uncini, Aurelio. - STAMPA. - 2015:(2015), pp. 1-8. (Intervento presentato al convegno International Joint Conference on Neural Networks, IJCNN 2015 tenutosi a Killarney, Repubblica di Irlanda nel 12-17 luglio 2015) [10.1109/IJCNN.2015.7280333].

Distributed music classification using random vector functional-link nets

SCARDAPANE, SIMONE;PANELLA, Massimo;UNCINI, Aurelio
2015

Abstract

In this paper, we investigate the problem of music classification when training data is distributed throughout a network of interconnected agents (e.g. computers, or mobile devices), and it is available in a sequential stream. Under the considered setting, the task is for all the nodes, after receiving any new chunk of training data, to agree on a single classifier in a decentralized fashion, without reliance on a master node. In particular, in this paper we propose a fully decentralized, sequential learning algorithm for a class of neural networks known as Random Vector Functional-Link nets. The proposed algorithm does not require the presence of a single coordinating agent, and it is formulated exclusively in term of local exchanges between neighboring nodes, thus making it useful in a wide range of realistic situations. Experimental simulations on four music classification benchmarks show that the algorithm has comparable performance with respect to a centralized solution, where a single agent collects all the local data from every node and subsequently updates the model.
2015
International Joint Conference on Neural Networks, IJCNN 2015
feature extraction; silicon; multiagent systems
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Distributed music classification using random vector functional-link nets / Scardapane, Simone; Fierimonte, R.; Wang, D.; Panella, Massimo; Uncini, Aurelio. - STAMPA. - 2015:(2015), pp. 1-8. (Intervento presentato al convegno International Joint Conference on Neural Networks, IJCNN 2015 tenutosi a Killarney, Repubblica di Irlanda nel 12-17 luglio 2015) [10.1109/IJCNN.2015.7280333].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/906280
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