http://www.cnr.it/ontology/cnr/individuo/prodotto/ID20664
Deterministic learning for maximum likelihood estimation through neural networkss (Articolo in rivista)
- Type
- Label
- Deterministic learning for maximum likelihood estimation through neural networkss (Articolo in rivista) (literal)
- Anno
- 2008-01-01T00:00:00+01:00 (literal)
- Alternative label
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- C. Cervellera, D. Macciò, M. Muselli (literal)
- Pagina inizio
- Pagina fine
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
- Rivista
- Note
- ISI Web of Science (WOS) (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- C. Cervellera, D. Macciò: Istituto di Studi sui Sistemi Intelligenti per l'Automazione, Consiglio Nazionale delle Ricerche (literal)
- Titolo
- Deterministic learning for maximum likelihood estimation through neural networkss (literal)
- Abstract
- A new efficient technique for estimating probability densities from data through the application of the approximate global maximum likelihood (AGML) approach is proposed. It employs a composition of kernel functions to estimate the correct behavior of parameters involved in the expression of the unknown probability density. Convergence to the optimal solution is guaranteed by a deterministic learning framework when low discrepancy sequences are used to generate the centers of the kernels. Trials on mixture of Gaussians show that the proposed semi-local technique is able to efficiently approximate the maximum likelihood solution even in complex situations where implementations based on standard neural networks require an excessive computational cost. (literal)
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