radial basis function

Multivariate functions {radial basis function} (RBF) can be weighted sums of independent linear functions.

input

Inputs can be spatial coordinates, angles, line-segment lengths, colors, segment configurations, feature binocular disparities, or texture descriptions. Training uses input data points.

dimensions

Data points have distances from coordinate means: |x - t|, where x are data-point coordinate values, and t are coordinate means. Data typically has Gaussian distribution, which can be broad or narrow, along all dimensions. Dimension number is typically less than data-point number.

training

Training assigns weights to dimensions or factors.

test sum

Test data point has sum over all weighted dimensions. Comparing sum to input data-object sums can identify test object. For narrow Gaussian distributions, RBF is like lookup table, because test objects only match if input equals mean.

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3-Computer Science-System Analysis-Functions

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Date Modified: 2022.0224