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  #1  
Old 10-03-2012, 08:13 AM
mileschen mileschen is offline
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Default 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!
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Old 10-03-2012, 12:17 PM
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magdon magdon is offline
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Default 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?

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Originally Posted by mileschen View Post
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!
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  #3  
Old 10-03-2012, 07:21 PM
mileschen mileschen is offline
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Default Re: Clarification of Problem 3.1

Yes, thank you very much. It is quite clear now.
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Old 10-07-2013, 08:55 PM
i_need_some_help i_need_some_help is offline
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Default 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?
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Old 10-08-2013, 08:55 AM
i_need_some_help i_need_some_help is offline
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Default Re: Clarification of Problem 3.1

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Originally Posted by i_need_some_help View Post
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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