rangeregularized
Range regularization is a technique used in machine learning and signal processing to stabilize algorithms and improve their performance by constraining the range of certain parameters or values. This method is often applied when dealing with ill-posed problems or when a model is prone to overfitting. By limiting the acceptable values for a parameter, range regularization can prevent extreme or undesirable outcomes, leading to more robust and generalizable solutions.
The core idea behind range regularization is to introduce a penalty or constraint into the optimization process
This technique is particularly useful in areas like inverse problems, where small errors in data can lead