Bayesian Neural Networks for Industrial Appli ations
نویسنده
چکیده
Abstra t We demonstrate the advantages of using Bayesian neural networks in regression, inverse and lassi ation problems, whi h are ommon in industrial appli ations. The Bayesian approa h provides onsistent way to do inferen e by ombining the eviden e from data to prior knowledge from the problem. A pra ti al problem with neural networks is to sele t the orre t omplexity for the model, i.e., the right number of hidden units or orre t regularization parameters. The Bayesian approa h o ers e ient tools for avoiding over tting even with very omplex models, and fa ilitates estimation of the on den e intervals of the results. In this ontribution we review the Bayesian methods for neural networks and present omparison results from ase studies in predi tion of the quality properties of on rete (regression), ele tri al impedan e tomography (inverse problem) and forest s ene analysis ( lassi ation). The Bayesian networks provided onsistently better results than other methods.
منابع مشابه
Bayesian Neural Networks for Industrial Appli
Aki Vehtari and Jouko Lampinen Laboratory of Computational Engineering, Helsinki University of Te hnology P.O.Box 9400, FIN-02015 HUT, FINLAND SMCia/99 1999 IEEE Midnight-Sun Workshop on Soft Computing Methods in Industrial Appli ations Kuusamo, Finland, June 16 18, 1999 Abstra t We demonstrate the advantages of using Bayesian neural networks in regression, inverse and lassi ation problems, whi...
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