The book describes the development and performance of proximal
classifiers, a class of kernel-based regularized mean square error
type classifier that learns within the penalized modeling paradigm.
The name proximal classifier indicates the fact of classification
of a test pattern by its proximity either to a hyperplane or to a
class centroid. The basic idea of the nonparallel plane classifier
is to model each class of data by fitting separate hyperplane
through it. A computationally efficient binary Nonparallel Plane
Proximal Classifier (NPPC) is described in detail along with its
nonlinear extension. NPPC is also extended to classify multiclass
data. A new approach of multiclass data classification through
vector-valued regression technique by the proximity to a class
centroid is described in detail. These classifiers are applied to
discriminate cancerous tissue samples from gene microarray data.
The book provides a complete literature survey in the field of
Support Vector Machine (SVM). It includes mathematical models,
detailed solution procedures and algorithms of the different
proximal classifiers with hands-on examples and well-documented
MATLAB programs.
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