I am now searching for a long time on the internet and on papers for an answers of simple questions. Am I able to train a Support Vector Regression algorithm with different data sets? If yes, how is the approach called?
I have 10 times the same battery with different usage, temperature and capacity.
- Usage and temperature are features
(x_i,i)and capacity is the output
- Battery_1 till timepoint n:
[x_1,1 y_1,1; ... ;x_1,n y_1,n]
- Battery_10 till timepoint n:
[x_10,1 y_10,1; ... ;x_10,n y_10,n]
Now I want to train my SVR with these sets, where the samples within a set belong together. I want give the algorithm a set of usage and temperature where I don't know the capacity and my SVR should predict it as such :
Thank you very much for your help and input.
M=[x_1,1 ... x_1,n ... x_10,1 ... x_10,n]? (Analog the output matrix) Is there no loss of time information or loss of dependency within a set? $\endgroup$