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Old 02-21-2013, 07:22 AM
BojanVujatovic BojanVujatovic is offline
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Default Question about Generalization result of SV's

Hi, I want to compliment and thank Professor and others for the wonderful set of lectures and the textbook which explain the subject of Machine Learning with extraordinary ease and clarity.

I have a question about the Generalization result of SV's, in Lecture 14, slide 20. It says that:
E[E_{out}] = \frac{E[N_{SV}]}{N-1}
I don't understand how was this bound derived (from something like the VC bound or is it an observation)? Also I am interested in knowing does it hold for any classification problem that we apply SV's for?
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Old 02-21-2013, 07:39 AM
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yaser yaser is offline
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Default Re: Question about Generalization result of SV's

Quote:
Originally Posted by BojanVujatovic View Post
Hi, I want to compliment and thank Professor and others for the wonderful set of lectures and the textbook which explain the subject of Machine Learning with extraordinary ease and clarity.

I have a question about the Generalization result of SV's, in Lecture 14, slide 20. It says that:
E[E_{out}] = \frac{E[N_{SV}]}{N-1}
I don't understand how was this bound derived (from something like the VC bound or is it an observation)? Also I am interested in knowing does it hold for any classification problem that we apply SV's for?
Thank you for your kind words. The proof of the inequality makes a number of assumptions. You can find a version of it in Vapnik's book "Statistical Learning Theory."
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Old 02-21-2013, 01:49 PM
BojanVujatovic BojanVujatovic is offline
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Default Re: Question about Generalization result of SV's

OK, thank you!
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