Neeta Dsouza answered . 2024-04-26 10:17:26

 data        = training + validation + test
 design      = training + validation
 nondesign   = test
 nontraining = validation + test

TRAINING:

 1. Given input matrix, target matrix and training parameters, 
estimate the weights and biases.

 2. Performance estimate is biased because the same data is used for 
training and performance estimation.

 1. Choose nonweight parameters (e.g., learning rate, momentum 
constant, stopping epoch...) 

 2. Rank multiple designs

 3. Performance estimates slightly biased because the same data is 
used to choose parameters

TEST:

Used to obtain UNBIASED performance estimates of designs ranked by 
validation data.

BASIC ASSUMPTIONS:

 REGRESSION AND CLASSIFICATION:

 All data assumed to be randomly chosen from the same parent 
probability distribution.

 TIME SERIES

 All data segments assumed to have the same summary statistics, 
i.e., mean, variance 
and correlations

If you vary the initial pendulum angle, the angle has to be part of the input matrix AND training has to include the range of angles considered.

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