How to form the training set ?

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chaaru datta on 14 May 2022
Commented: chaaru datta on 20 Jun 2022 at 6:57
Hello all, I am new to machine learning and wanna use MATLAB for it... I am trying to form a training set in MATLAB on the basis of following expression:
where S denotes the training set, M = 10, m = 1 to M, is the training feature such that , denotes the training label such that .
My query is what should be the dimension of my training set. I think it should be .
Any help in this regard will be highly appreciated.
chaaru datta on 14 May 2022
Any help in this regard would be highly appreciated...

the cyclist on 14 May 2022
If I understand all of your notation correctly, I think your training set needs to be an Mx3 matrix.
If means that each observation of x has two components (epsilon minus and epsilon plus), then for each observation of the training set, you need two values to represent x, and one to represent y. So
M = [0.2 0.3 -1;
-0.3 0.4 1;
...
0.6 0.5 -1];
would be the representation in which
• 1st column is x (epsilon minus)
• 2nd column is x (epsilon plus)
• 3rd column is y
chaaru datta on 17 May 2022
Yes sir...you are right. I am generating the labels but they are not affected by the features.
Also, I would like to describe the system model given in paper in brief.
1) System model contains Radio frequency source, tag and reader. 2) Tag reflects (backscatters) two types of signal viz., -1 and +1. 3) When reflected signal from tag is -1 , then epsilon minus feature is obtained at reader else epsilon plus is obtained at the reader. 4) Thus my training set consists of epsilon minus, epsilon plus and labels for each reflected signal from the tag.

the cyclist on 17 May 2022
I spent a little bit more time with the paper.
It seems to me that in the paper, the labels y are supposed to be used when generating s (Eq. 5 & 6) and then epsilon (Eq. 7 & 8).
But you don't use your labels as part of the calculation of the features.
chaaru datta on 20 Jun 2022 at 6:57
Hello Sir, can you please share your insights on forming training set as done in this paper.