How to count m if the set of hypothesis is infinity?
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VC stated that eventhough the hypothesis space is infinite, as long as the sample is finite, then we can know how much the samples do we need.
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Proving lower bound is easier than proving an upper bound. Means that in this case we only need to to prove at least one example, than using max example
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For VC dimension, we need one example, but to prove that, test infinite number of hypothesis.
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In the vc dimension = 2, we know that we still get positive, in setting the limit [], between those two
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But in the case vc dimension = 3, one example exists that we can't use the VC, so therefore at least 2
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Here we have R2 dimension, and as an input, weight parameters wT*X and the output will be labeled by some hypothesis theta
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R2 will get 3 VC Dimensions.
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while R1 get 2VC, R2 get 3VC, does this get directly correlated by nVC = Rn-1?
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VC dimension directly correlated with the numbers of parameters.
