Bayesian learning techniques: Application to neural networks with constraints on weight space

A. Eleuteri, R. Tagliaferri, L. Milano, F. Acernese, M. De Laurentiis

Research output: Contribution to journalArticle

Abstract

In this paper the fundamentals of Bayesian learning techniques are shown, and their application to neural network modeling is illustrated. Furthermore, it is shown how constraints on weight space can easily be embedded in a Bayesian framework. Finally, the application of these techniques to a complex neural network model for survival analysis is used as a significant example.

Original languageEnglish
Pages (from-to)216-232
Number of pages17
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2486 LNCS
Publication statusPublished - 2002

Fingerprint

Bayesian Learning
Neural Networks
Neural networks
Network Modeling
Survival Analysis
Neural Network Model
Complex Networks
Framework

Keywords

  • Bayesian learning frameworks
  • Learning with constraints
  • Survival analysis

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

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abstract = "In this paper the fundamentals of Bayesian learning techniques are shown, and their application to neural network modeling is illustrated. Furthermore, it is shown how constraints on weight space can easily be embedded in a Bayesian framework. Finally, the application of these techniques to a complex neural network model for survival analysis is used as a significant example.",
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