New Idea From Andrew Ng

 

Overfit is a big problem --> assuming too many parameter space and high variance learning (not high bias)

Solution A:  constraints: e.g. regularization, sparsity (L1-regularization), ???

 

Solution B: Use a lot of data (10x or 100x),  how?  

artificial synthesis of data,  adaboost, random forest ?

 

Are they (more data and regularization) equivalent or behavior similarly?