# 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.

• Let me help understand your problem, you have 10 different datasets collected from same battery output and you want to feed these 10 data sets to a SVM model, correct? – Xformer Jul 31 '17 at 11:44
• These are ten different batteries form the exactly same type. So every set belongs together. The "function" behind all sets is the same. The Inputs are different and so the output for every battery is also different from the other. – i.k. Jul 31 '17 at 11:46
• Ok. as long as number of features you shouldn't be having problem them feeding to SVM. – Xformer Jul 31 '17 at 12:06
• Thank you for the answer. Does then my training input matrix look like this: 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? – i.k. Jul 31 '17 at 14:12