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Old 06-21-2013, 08:11 AM
skwong skwong is offline
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Join Date: Apr 2013
Location: Hong Kong
Posts: 13
Default Re: *ANSWER* Q14 about linearly separable by SVM

Sorry to annoy you again. Let me summarize my understanding point by point:

(1) In one sense, hard margin SVM is no different from simpler algorithm like
PLA for linearly separable data (albeit the result may be different,
they are the same in terms of generalization, Ein = 0, ...).

(2) Point (1) still apply for non-linear transformed data.

(3) In ML, in an attempt to find a separation plane (or line) is somehow
similar to find out the coefficient of an polynomial (in case a
polynomial is used as the hypothesis set), i.e., the w.

(3a) Although the coefficient of the polynomial will not be found in
explicit form, one can either view it as the data being transformed
to a different space (higher or lower dimensional (normally not necessary
to use lower dimension)) and separated linearly; alternatively, it can
be mapped back to the original space and interpret as an higher order
polynomial.

(4) This hold true for hard margin SVM, and for data explicity transformed
nonlinearly.

(5) From what I have done in Q14, with hard margin SVM + RBF kernel on 100
data points, it can always separate the data linearly (Ein = 0). And it
matches with my understanding.

Then, my question is: is RBF regular form not normally used for
supervised training ?

We learn a lot from the final exam paper about the RBF regular form.
As the performance is normally not as good as SVM, also we have no
cue about what is the best K. Does it mean, in supervised learning,
we normally will not consider to use RBF regular form ?
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