This paper proposes a new algorithm based on multiscale stochastic local search with binary representation for training neural networks [binary learning machine (BLM)]. We study the effects of neighborhood evaluation strategies, the effect of the number of bits per weight and that of the maximum weight range used for mapping binary strings to real values. Following this preliminary investigation, we propose a telescopic multiscale version of local search, where the number of bits is increased in an adaptive manner, leading to a faster search and to local minima of better quality. An analysis related to adapting the number of bits in a dynamic way is presented. The control on the number of bits, which happens in a natural manner in the proposed method, is effective to increase the generalization performance. The learning dynamics are discussed and validated on a highly nonlinear artificial problem and on real-world tasks in many application domains; BLM is finally applied to a problem requiring either feedforward or recurrent architectures for feedback control.

A Telescopic Binary Learning Machine for Training Neural Networks / Brunato, Mauro; Battiti, Roberto. - In: IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS. - ISSN 2162-237X. - STAMPA. - 28:3(2017), pp. 665-677. [10.1109/TNNLS.2016.2537300]

A Telescopic Binary Learning Machine for Training Neural Networks

Brunato, Mauro;Battiti, Roberto
2017-01-01

Abstract

This paper proposes a new algorithm based on multiscale stochastic local search with binary representation for training neural networks [binary learning machine (BLM)]. We study the effects of neighborhood evaluation strategies, the effect of the number of bits per weight and that of the maximum weight range used for mapping binary strings to real values. Following this preliminary investigation, we propose a telescopic multiscale version of local search, where the number of bits is increased in an adaptive manner, leading to a faster search and to local minima of better quality. An analysis related to adapting the number of bits in a dynamic way is presented. The control on the number of bits, which happens in a natural manner in the proposed method, is effective to increase the generalization performance. The learning dynamics are discussed and validated on a highly nonlinear artificial problem and on real-world tasks in many application domains; BLM is finally applied to a problem requiring either feedforward or recurrent architectures for feedback control.
2017
3
Brunato, Mauro; Battiti, Roberto
A Telescopic Binary Learning Machine for Training Neural Networks / Brunato, Mauro; Battiti, Roberto. - In: IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS. - ISSN 2162-237X. - STAMPA. - 28:3(2017), pp. 665-677. [10.1109/TNNLS.2016.2537300]
File in questo prodotto:
File Dimensione Formato  
TNNLS-2015-P-5760.pdf

accesso aperto

Descrizione: Testo e immagini corrispondenti alla versione a stampa.
Tipologia: Post-print referato (Refereed author’s manuscript)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 6.62 MB
Formato Adobe PDF
6.62 MB Adobe PDF Visualizza/Apri
tnnls2016a.pdf

accesso aperto

Descrizione: Appendice collegata al testo
Tipologia: Altro materiale allegato (Other attachments)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 238.77 kB
Formato Adobe PDF
238.77 kB Adobe PDF Visualizza/Apri
07436772.pdf

Solo gestori archivio

Tipologia: Versione editoriale (Publisher’s layout)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 3.01 MB
Formato Adobe PDF
3.01 MB Adobe PDF   Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/170648
Citazioni
  • ???jsp.display-item.citation.pmc??? 0
  • Scopus 3
  • ???jsp.display-item.citation.isi??? 0
  • OpenAlex ND
social impact