Neural Networks (NNs) had wide interest due to empirical achievements on a wide range of learning issues. NNs are highly expressive models that can learn complex function approximations from input/output, with a particular ability to train them on massive data sets with stochastic optimization. The Bayesian approach to NNs can potentially avoid some of the problems of stochastic optimization. The use of Bayesian learning is well suited to the problem of real estate appraisals, in fact, Bayesian inference techniques are very interesting in order to deal with a small and noisy sample in the field of probabilistic inference carried out with neural model. For this purpose it has here been experimented a NNs model with Bayesian learning. The output distribution has been calculated operating a numerical integration on the weights space with the help of Markov Chain Hybrid Monte Carlo Method.

Genetic algorithms for real estate market analysis

Fabiana Forte
Membro del Collaboration Group
2017

Abstract

Neural Networks (NNs) had wide interest due to empirical achievements on a wide range of learning issues. NNs are highly expressive models that can learn complex function approximations from input/output, with a particular ability to train them on massive data sets with stochastic optimization. The Bayesian approach to NNs can potentially avoid some of the problems of stochastic optimization. The use of Bayesian learning is well suited to the problem of real estate appraisals, in fact, Bayesian inference techniques are very interesting in order to deal with a small and noisy sample in the field of probabilistic inference carried out with neural model. For this purpose it has here been experimented a NNs model with Bayesian learning. The output distribution has been calculated operating a numerical integration on the weights space with the help of Markov Chain Hybrid Monte Carlo Method.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11591/386907
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