Support Vector Table understanding

elgolubeva

Good evening!

1. Could you please explain what does "function value" in Support Vector Table mean?

2. I know that for every attribute its weight w = sum_{i=1}^{i=N} (alpha_i * x_i).

 It seems that the weight from Weight Table is equal to such sum, where alpha_i and x_i are values from Support Vector Table.

Why are the attrubute values x_i multiplied in this table on some coefficients? For example in the input example set the attribute "Outlook=rain" had values:

0.0

0.0

0.0

1.0

1.0

1.0

and in Support Vector Table this attribute has values:

0.0

0.0

0.0

1.3416407864998736

1.3416407864998736

1.3416407864998736

.

3. I tried to understand the meaning of alpha here: 

https://www.quora.com/What-does-the-alpha-mean-in-the-dual-form-of-the-SVM-optimization-problem.

It is said: "if the positive training example has been correctly classified above the margin by the optimal w, the corresponding alpha = 0".

Is the converse true: if alpha = 0 then the positive training example has been correctly classified above the margin by the optimal w? 

 

How can I use information about alpha values and support vectors to adjust the SVM operator and to get better performance?

 

Thank you very much for the help.

 

 

elgolubeva

Good morning!

I found an answer to the question 3 myself.

 

But could you please explain what does "function value" in Support Vector Table mean? And how it can be obtained from other columns of Support Vector Table?

 

Thanks in advance.

Elizaveta