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CS 536 : Support Vector Machine Problems 

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CS 536 : Support Vector Machine Problems
1) Suppose you had a data set in two dimensions that satisfied the following: the positive class all lay within a
certain radius of a point, the negative class all lay outside that radius.
– Show that under the feature map φ(x1, x2) = (1, x1, x2, x1x2, x2
1
, x2
2
) (or equivalently, with the kernel
K(x, y) = (1 + x.y)
2
), a linear separator can always be found in this embedded space, regardless of radius
and where the data is centered.
– In fact show that if there is an ellipsoidal separator, regardless of center, width, orientation (and dimension!), a separator can be found in the quadratic feature space using this kernel.
2) As an extension of the previous problem, suppose that the two dimensional data set satisfied the following: the
positive class lay within one of two (disjoint) ellipsoidal regions, and the negative class was everywhere else.
Argue that the kernel K(x, y) = (1 + x.y)
4 will recover a separator.
3) Suppose that the two dimensional data set is distributed like the following: the positive class lays in a circle
centered at some point, the negative class lies in a circular band surrounding it of some radius, and then
additional positive points lie outside that radius. Argue that the kernel K(x, y) = (1 + x.y)
4 will recover a
separator.
4) Consider the XOR data (located at (±1, ±1)). Express the dual SVM problem and show that a separator can
be found using
– K(x, y) = (1 + x.y)
2
– K(x, y) = exp(−||x − y||2
).
For each, determine the regions of (x1, x2) space where points will be classified as positive or negative. Given
that each produces a distinct separator, how might you decide which of the two was preferred?
1

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