NEURAL NETWORK SUBSET TERMINOLOGY(comp.ai.neural-nets):
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.
VALIDATION:
Used with multiple designs to
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.
Hope this helps.
Thank you for formally accepting my answer
Greg
