Solved – Normalizing SVM predications to [0,1]

I have trained an linear SVM which takes a pair of objects, computes features and is expected to learn a semantic similarity function between objects(we can say that it predicts whether the two objects are similar enough that they should be merged or not). The problem I am facing is that the predictions can be from $-infty$ to $infty$ and I need a score from [0,1] as a semantic similarity.

One suggestion that I received was that use min-max normalization to normalize the scores. Is there a better way(which is more generic rather than depending on values of min and max from training data)? Please mention the assumptions also, in case your method has any.


Use a sigmoid function $y = frac{1}{(1+e^{-f(x)})}$ where $f(x)$ is your output [-inf, inf], then y will be between [0,1]. This is a known techinique, see for example multilayer perceptron (MLP)

Similar Posts:

Rate this post

Leave a Comment