也是一篇讨论“置信度”的文章。将二分类问题转化为一个分别独立的“三分类”问题，且分类边界可学习，而边界的取定用 SVM。文章里似乎有不少详尽的数学理论推导，值得练手推推看~

checker board classification example

The margin seems to be manually set by the creator in the loss function. In the sample code, the margin is 1-- so the incorrect class has to be scored lower than the correct class by 1.

How is this margin determined? It seems like one would have to know the magnitude of the scores beforehand.

Diving deeper, is the scoring magnitude always the same if the parameters are normalized by their average and scaled to be between 0 and 1? (or -1 and -1... not sure of the correct scaling implementation)

Coming back to the topic -- is this 'minimum margin' or delta a tune-able parameter?

What effects do we see on the model by adjusting this parameter?

What are best and worst case scenarios of playing with this parameter?