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It all depends on the frames and settings . 70% to 95%.
Yes everything he shows and reversals and strength .
The evidence is very welcome. It is big news that it is possible to predict on unsteady BP sections. You are the only one who claims this, I am not familiar with others.
Proving anything is not a worthwhile endeavour.
It's amazing that there are people on the market who are proud of it. I mean, you can show a tester run with a graph. Or is everything you've written just heat rubbish?
This DA is quite weak.
Ran it on a simple classic recognition example:
Example strings:
1. Bird
2. Fly
3. Aeroplane
4. Glider
5. Non-winged rocket
The first six columns are inputs of recognisable objects. The rest of the columns are outputs.
A two-layer grid: 6 x 2 x 6 x 6
When tested with Back Propagation it's a real bummer, because 40% of training sample are linear separability, if the error is less than 0.01, then the training sample is considered recognized.
So, neither an aeroplane, glider or rocket were not recognized, all outputs have only negative values with any inputs. The bird and glider are recognized accurately enough. The output of differences between biological objects and mechanical objects was also recognized quite accurately.
When testing RPROP under the same conditions and the same architecture, the results are better:
So here the linear separability is already 100%, but errors are present.