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Welcome to LabVIEW Machine Learning Toolkit

I would like to generate classification function coefficients from multiple classes with multiple variables.

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Message 11 of 41
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How many variables and how many samples do you have in the application? Does neural network work?

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Message 12 of 41
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A dozen to two dozen classes, several thousand samples, and 70 variables. Neural networks would do that but discrininant function analysis can provide a measure of nearness to the class centroid that I'd like to have when classifying unknown samples.

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Message 13 of 41
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Neural network can do the same. It does not give you an explicit formula as a classifier. But you can use it as a 'black box'.

Example_BP Network_Classification shows how to do that. 20% of the samples are pretended to be 'unknown', and tested in the fourth step with the 'black box' generated in the third step.

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Message 14 of 41
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Yes, but because it is a black box, any classification made using it does not provide any measure of how the outputted classification compares with the know training set. With DFA you can calculate the nearness to the centroid of the training set(s). I need that information in addition to a classification.

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Message 15 of 41
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I see your point. Maybe the direct output from NN, as marked in the attached figure, could be useful to you.

NN.JPG

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Message 16 of 41
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