Bayesian Methods for Neural Networks

Bayesian Methods for Neural Networks (1999)

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by João F. G. De Freitas

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Datum	Value	Source
TITLE	Bayesian Methods for Neural Networks	SVM HeaderParse 0.2
AUTHOR NAME	João F. G. De Freitas	SVM HeaderParse 0.2
AUTHOR AFFIL	Trinity College, University of Cambridge; and; Cambridge University Engineering Department.; Dissertation submitted to the University of Cambridge; for the degree of Doctor of Philosophy	SVM HeaderParse 0.2
ABSTRACT	Summary The application of the Bayesian learning paradigm to neural networks results in a flexi-ble and powerful nonlinear modelling framework that can be used for regression, den-sity estimation, prediction and classification. Within this framework, all sources of uncertainty are expressed and measured by probabilities. This formulation allows for a probabilistic treatment of our a priori knowledge, domain specific knowledge, model selection schemes, parameter estimation methods and noise estimation techniques. Many researchers have contributed towards the development of the Bayesian learn-ing approach for neural networks. This thesis advances this research by proposing several novel extensions in the areas of sequential learning, model selection, optimi-sation and convergence assessment. The first contribution is a regularisation strategy for sequential learning based on extended Kalman filtering and noise estimation via evidence maximisation. Using the expectation maximisation (EM) algorithm, a similar algorithm is derived for batch learning. Much of the thesis is, however, devoted to Monte Carlo simulation methods. A robust Bayesian method is proposed to estimate,	SVM HeaderParse 0.2
YEAR	1999	INFERENCE
VENUE TYPE	TECHREPORT	INFERENCE
TECH	PhD thesis	INFERENCE
CITATIONS	77 found	ParsCit 1.0