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Shape-adaptive radial basis functions.

A R Webb¹, S Shannon

¹Defence Evaluation and Research Agency, Malvern, Worcestershire WR14 3PS, UK.

IEEE Transactions on Neural Networks

|February 8, 2008

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This study explores optimal radial basis functions for regression and discrimination tasks. An iterative strategy using conjugate gradients and nonparametric smoothing is presented for improved function estimation in various applications.

Area of Science:

Machine Learning
Statistical Modeling
Data Analysis

Background:

Radial basis functions (RBFs) are established tools for regression and discrimination.
Previous applications show RBFs have considerable success across diverse fields.
Optimal selection of RBF form remains a key challenge.

Purpose of the Study:

To investigate the optimal form of radial basis functions for regression and discrimination.
To develop an iterative strategy for function estimation in regression.
To apply optimal scaling concepts within a discrimination framework.

Main Methods:

An iterative strategy using a conjugate gradient-based algorithm.
Integration of a nonparametric smoother for function estimation.

Related Experiment Videos

Development within a discrimination framework leveraging optimal scaling.

Main Results:

The proposed iterative strategy was tested on simulated and real data sets.
Performance evaluation across a range of data scenarios.
Demonstration of the method's applicability in practical settings.

Conclusions:

The study presents an effective iterative strategy for RBF-based regression and discrimination.
Optimal function forms can be determined using the proposed conjugate gradient and smoothing approach.
The method shows promise for various data analysis applications.