Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Videos

Bayesian wavelet networks for nonparametric regression.

C C Holmes¹, B K Mallick

¹Department of Mathematics, Imperial College, London, SW7 2BZ, UK. c.holmes@ic.ac.uk

IEEE Transactions on Neural Networks

|February 6, 2008

Summary

This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

A Conditional Density Estimation Partition Model Using Logistic Gaussian Processes.

Biometrika·2020

Same author

Systemic anti-inflammatory therapy aided by double-headed nanoparticles in a canine model of acute intraocular inflammation.

Science advances·2020

Same author

A general framework for updating belief distributions.

Journal of the Royal Statistical Society. Series B, Statistical methodology·2016

Same author

Quantification of subclonal distributions of recurrent genomic aberrations in paired pre-treatment and relapse samples from patients with B-cell chronic lymphocytic leukemia.

Leukemia·2012

Same author

CNV discovery using SNP genotyping arrays.

Cytogenetic and genome research·2009

Same author

Generalized monotonic regression using random change points.

Statistics in medicine·2003

Same journal

Universal perceptron and DNA-like learning algorithm for binary neural networks: LSBF and PBF implementations.

IEEE transactions on neural networks·2013

Same journal

Guest editorial: special section on white box nonlinear prediction models.

IEEE transactions on neural networks·2011

Same journal

Data-based fault-tolerant control of high-speed trains with traction/braking notch nonlinearities and actuator failures.

IEEE transactions on neural networks·2011

Same journal

Guest editorial: special section on data-based control, modeling, and optimization.

IEEE transactions on neural networks·2011

Same journal

Neural network-based multiple robot simultaneous localization and mapping.

IEEE transactions on neural networks·2011

Same journal

Data-driven model-free adaptive control for a class of MIMO nonlinear discrete-time systems.

IEEE transactions on neural networks·2011

See all related articles

Bayesian radial wavelet networks offer a novel approach to nonparametric regression. This method adapts model complexity to data, improving predictions without explicit network selection.

Area of Science:

Machine Learning
Statistical Modeling
Nonparametric Regression

Background:

Radial wavelet networks are a recent advancement for nonparametric regression.
Evaluating their performance within a Bayesian framework is crucial for understanding their capabilities.

Purpose of the Study:

To analyze the performance of radial wavelet networks using a Bayesian approach.
To develop a method that bypasses the need for explicit network testing or selection.

Main Methods:

Deriving probability distributions over network dimensions and coefficients.
Placing a prior on the degrees of freedom of the model.
Forming predictions by mixing models of varying dimensions and parameterizations.

Related Experiment Videos

Main Results:

Demonstrated adaptive model complexity that matches data complexity.
Achieved good performance on benchmark test series.
Provided a robust Bayesian framework for radial wavelet networks.

Conclusions:

The Bayesian framework offers an effective way to utilize radial wavelet networks.
Adaptive complexity leads to improved performance in nonparametric regression tasks.
This approach provides a principled method for model selection and prediction.