Approximate Bayesian MLP regularization for regression in the presence of noise.
Jung Guk ParkSungho JoPublished in: Neural Networks (2016)
Keyphrases
- gaussian processes
- sparse bayesian learning
- simple linear
- kernel ridge regression
- regression model
- relevance vector machine
- maximum entropy discrimination
- reproducing kernel hilbert space
- noise level
- artificial neural networks
- model selection
- radial basis function network
- maximum likelihood
- bayesian learning
- multi layer perceptron
- bayesian networks
- multilayer perceptron
- neural network
- noise reduction
- linear combination
- bayesian estimation
- prior information
- image prior
- wiener filter
- additive noise
- feature selection
- signal to noise ratio
- linear regression
- regression algorithm
- knn
- gradient boosting
- model averaging
- parameter selection
- noise model
- regression problems
- back propagation
- gaussian noise
- median filter
- rbf network
- support vector regression
- bayesian inference
- noisy data
- radial basis function