Support Vector Regression trained with data sets

Question

Support Vector Regression trained with data sets

i.k.

2022年4月30日 23:04

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.

Topic svm predictive-modeling machine-learning

Category Data Science

Xformer · Accepted Answer · 2017年8月1日 09:06

Add one more feature in dataset which will identify battery, with this your dataset will have 3 features battery,usage, temperature and one target variable capacity. Test this out if you are losing the information then create different models for each data set.

Andreas Look · Accepted Answer · 2017年7月31日 15:04

1

Andreas Look answered at 2017年7月31日 15:04

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).

Support Vector Regression trained with data sets

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