i just read the chessprogramming article about Texels tuning algorithm and found the following part which i do not understand.

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```
Sigmoid(s)=1/(1+10^(-K/400))
K is a scaling constant.
Compute the K that minimizes E. K is never changed again by the algorithm.
```

My error computation attempts only pass an evaluation score to the sigmoid function. So, if i get the result of 0.6 i can square the difference to 0.0/0.5/1.0 or just another reference value computed by the sigmoid function.

So, how does it help, if i just want the minimized sum of squared errors ?

Thanks in advance.