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Old 10-08-2013, 07:43 PM
luwei0917 luwei0917 is offline
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Default Problem 3.6

for part (a). why it is \ge 1 instead of 0? Thanks.
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Old 10-10-2013, 09:15 AM
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magdon magdon is offline
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Default Re: Problem 3.6

The point is to show that weights giving >0 implies the existence of weights giving >=1.
Originally Posted by luwei0917 View Post
for part (a). why it is \ge 1 instead of 0? Thanks.
Have faith in probability
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Old 10-09-2014, 10:09 AM
squidsforbreakfast squidsforbreakfast is offline
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Default Re: Problem 3.6

I think I have an answer for parts b and c, but I'm not really sure if what I'm doing is correct. Can I check it with you?
For b), the thing we're trying to optimize is w, so then w would be z. So then we want to multiply the weights of the data (A) with w (z) to ensure that they are less than the signs of the data (y). I'm pretty sure that's right, however it feels a little hand-wavy to me, so even if it is right, I'm worried I may be missing some of the underlying logic of why it is the right answer.
For c), then, we want to minimize error, so then I think (c^T)z is the summation of the errors, b=y(w^Tx) and Az = 1-e (I couldn't figure out how to write xi in here).
But ten we have z being related to the summation of the errors in one equation, but related to just a single error instance in the other, so I think that what I have can't be right, even if it makes sense to me.
Can you offer a hint on what I'm doing wrong here?
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