Support Vector Regression trained with data sets 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 (y_i,i).
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 : x_d --> y_d.

Thank you very much for your help and input.
Why not concatenating the 10 datasets you have and simply creating one big dataset?
Alternatively you could train 10 seperate SVMs and let them vote (for examples: output = mean of the 10 SVMs).