Nonlinear SVMs

With what we have presented so far, data sets that are linearly separable (perhaps with a few exceptions or some noise) are well-handled. But what are we going to do if the data set just doesn't allow classification by a linear classifier? Let us look at a one-dimensional case. The top data set in Figure 15.6 is straightforwardly classified by a linear classifier but the middle data set is not. We instead need to be able to pick out an interval. One way to solve this problem is to map the data on to a higher dimensional space and then to use a linear classifier in the higher dimensional space. For example, the bottom part of the figure shows that a linear separator can easily classify the data if we use a quadratic function to map the data into two dimensions (a polar coordinates projection would be another possibility). The general idea is to map the original feature space to some higher-dimensional feature space where the training set is separable. Of course, we would want to do so in ways that preserve relevant dimensions of relatedness between data points, so that the resultant classifier should still generalize well.

SVMs, and also a number of other linear classifiers, provide an easy and
efficient way of doing this mapping to a higher dimensional space, which
is referred to as ``the *kernel trick* ''. It's not really a
trick: it just exploits the math that we have seen. The SVM linear
classifier relies on a dot product between data point vectors. Let
. Then the
classifier we have seen so far is:

**Worked example.** The quadratic kernel in two dimensions.quad-kernel
For 2-dimensional vectors
,
, consider
. We wish to show that this is a
kernel, i.e., that
for some
. Consider
. Then:

In the language of functional analysis, what kinds of functions are
valid *kernel functions* ? Kernel functions are
sometimes more precisely referred to as *Mercer
kernels* , because they must satisfy Mercer's
condition: for any such that
is finite, we must have that:

(177) |

The two commonly used families of kernels are polynomial kernels and radial basis functions. Polynomial kernels are of the form . The case of is a linear kernel, which is what we had before the start of this section (the constant 1 just changing the threshold). The case of gives a quadratic kernel, and is very commonly used. We illustrated the quadratic kernel in quad-kernel.

The most common form of radial basis function is a Gaussian
distribution, calculated as:

(178) |

The world of SVMs comes with its own language, which is rather different
from the language otherwise used in machine learning. The terminology
does have deep roots in mathematics, but
it's important not to be too awed
by that terminology. Really, we are talking about some quite
simple things. A polynomial kernel allows us to model feature
conjunctions (up to the order of the polynomial). That is, if we
want to be able to model occurrences of pairs of words, which give
distinctive information about topic classification, not given by the
individual words alone, like perhaps
*operating* and *system* or *ethnic* and
*cleansing*, then we need to use a quadratic kernel. If
occurrences of triples of words give distinctive information, then we
need to use a cubic kernel.
Simultaneously you
also get the powers of the basic features - for most text applications,
that probably isn't useful, but just comes along with the math and
hopefully doesn't do harm. A radial basis function allows you to have
features that pick out circles (hyperspheres) - although the decision
boundaries become much more complex as multiple such features interact.
A string kernel lets you have features that are character
subsequences of terms. All of these are straightforward notions which
have also been used in many other places under different names.

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2009-04-07