Title: Support Feature Machines,
Abstract: Support Vector Machines (SVMs) with various kernels have played dominant role
in machine learning for many years, finding numerous applications. Although
they have many attractive features interpretation of their solutions is quite
difficult, the use of a single kernel type may not be appropriate in all areas
of the input space, convergence problems for some kernels are not uncommon, the
standard quadratic programming solution has $O(m^3)$ time and $O(m^2)$ space
complexity for $m$ training patterns. Kernel methods work because they
implicitly provide new, useful features. Such features, derived from various
kernels and other vector transformations, may be used directly in any machine
learning algorithm, facilitating multiresolution, heterogeneous models of data.
Therefore Support Feature Machines (SFM) based on linear models in the extended
feature spaces, enabling control over selection of support features, give at
least as good results as any kernel-based SVMs, removing all problems related
to interpretation, scaling and convergence. This is demonstrated for a number
of benchmark datasets analyzed with linear discrimination, SVM, decision trees
and nearest neighbor methods.