#1




Input normalization
I could use some clarification on the 9th homework assignment.
In normalizing the features I would assume that it is with respect to the bounds on the feature's possible values rather than on the data itself? For example, the minimum pixel intensity is for the digit data is 1 and the maximum is +1 therefore, using average pixel intensity as a feature, the min and max average pixel intensities that are possible are 1 and +1 (if all pixels were white or all pixels were black for a given data point). So in this case I am under the assumption that the feature doesn't need any shift or scale normalization since the min and max values are already within the [1,1] bound regardless of the data. Is this correct? 
#2




Re: Input Normalization
Feature normalization (as in the example of data snooping in financial trading in chapter 5) is based on the data itself. Similarly in the assignment, the feature normalization is based on the data.
Quote:
__________________
Have faith in probability 
#3




Plotting Separators
Hi Professor, since the regression will result a highly nonlinear boundary how do we plot it in the feature plot?Thanks!

#4




Re: Plotting Separators
You just need to show the region of your feature space that is classified as +1 versus 1. This can be done by griding your space and plotting a blue dot for +1 and a red dot for 1.
__________________
Have faith in probability 
Thread Tools  
Display Modes  

