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-   -   Clarification of Problem 3.1 (http://book.caltech.edu/bookforum/showthread.php?t=1901)

mileschen 10-03-2012 07:13 AM

Clarification of Problem 3.1
 
Actually, I could not understand what the learning task of the double semi-circle? It just said that there are two semi-circles of width thk with inner radius rad, separated by sep as shown (red -1 and blue is +1). Then, when we generate the 2000 examples, should we also give each example a corresponding color?

Could anyone possibly demonstrate this question clearly for me? Thanks very much!

magdon 10-03-2012 11:17 AM

Re: Clarification of Problem 3.1
 
The problem describes a geometrical region on the plane, half of which is shaded red and the other half blue. You generate 2000 points in this region randomly. If the point (x_1,x_2) lands in the blue region, it is classified blue(+1) and otherwise red(-1). In this way you generate a training data set of 2000 points.

Does that clarify your confusion?

Quote:

Originally Posted by mileschen (Post 6006)
Actually, I could not understand what the learning task of the double semi-circle? It just said that there are two semi-circles of width thk with inner radius rad, separated by sep as shown (red -1 and blue is +1). Then, when we generate the 2000 examples, should we also give each example a corresponding color?

Could anyone possibly demonstrate this question clearly for me? Thanks very much!


mileschen 10-03-2012 06:21 PM

Re: Clarification of Problem 3.1
 
Yes, thank you very much. It is quite clear now.

i_need_some_help 10-07-2013 07:55 PM

Re: Clarification of Problem 3.1
 
I would like some clarification as well.

Does part (b) involve running the pocket algorithm (it says, in parentheses, "for classification") or would just running the 1-iteration linear regression algorithm suffice?

i_need_some_help 10-08-2013 07:55 AM

Re: Clarification of Problem 3.1
 
Quote:

Originally Posted by i_need_some_help (Post 11552)
I would like some clarification as well.

Does part (b) involve running the pocket algorithm (it says, in parentheses, "for classification") or would just running the 1-iteration linear regression algorithm suffice?

I made a mistake and was running regression with input prepared for regression, not classification. No need for pocket. :)


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