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Abstract:
Suppose you are given some dataset drawn from an underlying
probability distribution
P
and you want to estimate a subset
S
of input space such that the probability that a test point drawn
from
P
lies outside of
S
is bounded by some a priori specified
0
v <
1. We propose an algorithm which approaches this problem by trying
to estimate a function
f
which is positive on
S
and negative on the complement. The functional form of
f
is given by a kernel expansion in terms of a potentially small
subset of the training data; it is regularized by controlling the
length of the weight vector in an associated feature space. The
algorithm is a natural extension of the support vector algorithm to
the case of unlabelled data.
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