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34
Neural networks for classification: a survey
 and Cybernetics  Part C: Applications and Reviews
, 2000
"... Abstract—Classification is one of the most active research and application areas of neural networks. The literature is vast and growing. This paper summarizes the some of the most important developments in neural network classification research. Specifically, the issues of posterior probability esti ..."
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Abstract—Classification is one of the most active research and application areas of neural networks. The literature is vast and growing. This paper summarizes the some of the most important developments in neural network classification research. Specifically, the issues of posterior probability estimation, the link between neural and conventional classifiers, learning and generalization tradeoff in classification, the feature variable selection, as well as the effect of misclassification costs are examined. Our purpose is to provide a synthesis of the published research in this area and stimulate further research interests and efforts in the identified topics. Index Terms—Bayesian classifier, classification, ensemble methods, feature variable selection, learning and generalization, misclassification costs, neural networks. I.
Robust Full Bayesian Learning for Radial Basis Networks
, 2001
"... We propose a hierachical full Bayesian model for radial basis networks. This model treats the model dimension (number of neurons), model parameters,... ..."
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Cited by 29 (4 self)
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We propose a hierachical full Bayesian model for radial basis networks. This model treats the model dimension (number of neurons), model parameters,...
Sequential Monte Carlo Methods For Optimisation Of Neural Network Models
, 1998
"... We discuss a novel strategy for training neural networks using sequential Monte Carlo algorithms and propose a new hybrid gradient descent/sampling importance resampling algorithm (HySIR). In terms of both computational time and accuracy, the hybrid SIR is a clear improvement over conventional seque ..."
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Cited by 14 (0 self)
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We discuss a novel strategy for training neural networks using sequential Monte Carlo algorithms and propose a new hybrid gradient descent/sampling importance resampling algorithm (HySIR). In terms of both computational time and accuracy, the hybrid SIR is a clear improvement over conventional sequential Monte Carlo techniques. The new algorithm may be viewed as a global optimisation strategy, which allows us to learn the probability distributions of the network weights and outputs in a sequential framework. It is well suited to applications involving online, nonlinear and nonGaussian signal processing. We show how the new algorithm outperforms extended Kalman filter training on several problems. In particular, we address the problem of pricing option contracts, traded in financial markets. In this context, we are able to estimate the onestepahead probability density functions of the options prices.
Bayesian neural network approaches to ovarian cancer identification from highresolution mass spectrometry data
 BIOINFORMATICS
, 2005
"... ..."
Robust Full Bayesian Learning for Neural Networks
, 1999
"... In this paper, we propose a hierarchical full Bayesian model for neural networks. This model treats the model dimension (number of neurons), model parameters, regularisation parameters and noise parameters as random variables that need to be estimated. We develop a reversible jump Markov chain Monte ..."
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Cited by 12 (9 self)
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In this paper, we propose a hierarchical full Bayesian model for neural networks. This model treats the model dimension (number of neurons), model parameters, regularisation parameters and noise parameters as random variables that need to be estimated. We develop a reversible jump Markov chain Monte Carlo (MCMC) method to perform the necessary computations. We find that the results obtained using this method are not only better than the ones reported previously, but also appear to be robust with respect to the prior specification. In addition, we propose a novel and computationally efficient reversible jump MCMC simulated annealing algorithm to optimise neural networks. This algorithm enables us to maximise the joint posterior distribution of the network parameters and the number of basis function. It performs a global search in the joint space of the parameters and number of parameters, thereby surmounting the problem of local minima. We show that by calibrating the full hierarchical ...
On Bayesian model assessment and choice using crossvalidation predictive densities
, 2001
"... We consider the problem of estimating the distribution of the expected utility of the Bayesian model (expected utility is also known as generalization error). We use the crossvalidation predictive densities to compute the expected utilities. We demonstrate that in flexible nonlinear models having ..."
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Cited by 7 (7 self)
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We consider the problem of estimating the distribution of the expected utility of the Bayesian model (expected utility is also known as generalization error). We use the crossvalidation predictive densities to compute the expected utilities. We demonstrate that in flexible nonlinear models having many parameters, the importance sampling approximated leaveoneout crossvalidation (ISLOOCV) proposed in (Gelfand et al., 1992) may not work. We discuss how the reliability of the importance sampling can be evaluated and in case there is reason to suspect the reliability of the importance sampling, we suggest to use predictive densities from the kfold crossvalidation (kfoldCV). We also note that the kfoldCV has to be used if data points have certain dependencies. As the kfoldCV predictive densities are based on slightly smaller data sets than the full data set, we use a bias correction proposed in (Burman, 1989) when computing the expected utilities. In order to assess the reliability of the estimated expected utilities, we suggest a quick and generic approach based on the Bayesian bootstrap for obtaining samples from the distributions of the expected utilities. Our main goal is to estimate how good (in terms of application field) the predictive ability of the model is, but the distributions of the expected utilities can also be used for comparing different models. With the proposed method, it is easy to compute the probability that one method has better expected utility than some other method. If the predictive likelihood is used as a utility (instead
Bees and Firefly Algorithms for Noisy NonLinear Optimisation Problems
 Proceedings of the International MultiConference of Enginers and Computer Scientists, Vol II
, 2011
"... Abstract — Effective methods for solving the complex and noisy engineering problems using a finite sequence of instructions can be categorised into optimisation and metaheuristics algorithms. The latter might be defined as an iterative search process that efficiently performs the exploration and exp ..."
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Cited by 7 (0 self)
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Abstract — Effective methods for solving the complex and noisy engineering problems using a finite sequence of instructions can be categorised into optimisation and metaheuristics algorithms. The latter might be defined as an iterative search process that efficiently performs the exploration and exploitation in the solution space aiming to efficiently find near optimal solutions. Various natural intelligences and inspirations have been adopted into the iterative process. In this work, two types of metaheuristics called Bees and Firefly algorithms were adapted to find optimal solutions of noisy nonlinear continuous mathematical models. Considering the solution space in a specified region, some models contain global optimum and multiple local optimums. Bees algorithm is an optimisation algorithm inspired by the natural foraging behaviour of honey bees. Firefly algorithm is used to produce a near optimal solution under a consideration of the flashing characteristics of fireflies. A series of computational experiments using each algorithm were conducted. Experimental results were analysed in terms of best solutions found so far, mean and standard deviation on both the actual yields and execution time to converge to the optimum. The Firefly algorithm seems to be better when the noise levels increase. The Bees algorithm provides the better levels of computation time and the speed of convergence. In summary, the Firefly algorithm is more suitable to exploit a search space by improving individuals ’ experience and simultaneously obtaining a population of local optimal solutions.