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Hi Mehrdad Ghomi,

Very interesting topic, very informative.

I have some questions for you:

1. In Modeling part, you changed 203 intervals into 25 to reduce the complexity. Will this actually affect the precision and the sensitivity of your model?

2.About your Hermitian reduction function, I can understand that by that you make the system less complex, but can you also add more evidence to show us how it improved the result?

3.For most the methods mentioned in this page are all theories, but did not touch how did you apply those methods in your training. Can you talk more about like how the pattern is formed?

4. About your K-NN, you mentioned that the use of 1-NN is to reduce the complexity, and it works better than others like 3-NN, I really want to know why it is better, and what are the differences between them?

Hi,

The reduction in the feature vector size didn't really affect the results, as the new re-modelled signal is pretty good in terms of results.
Yes, I can perhaps put the results without the modelling there too for the second draft!
Actually the reason that why the 1-NN is better is still a question mark for me, but the results are certain that it is better.

I really appreciate your positive comments and points, Thanks!