Evaluating the Performance of an Admissible Kernel Function in Banach Space for Binary Data
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Date
2013
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Abstract
Classification is a learning function that maps a
given data item into one of several predefined
classes. H is a data analysis technique that extracts
models describing important data classes and
predicts future values. Basically, classification
techniques have better capability to handle a wider
variety o f datasets than regression. It can also be
described as a supervised learning algorithm in the
machine learning process. Support Vector Machine
(SVM) is an emerging classifier based on supervised
machine learning approach. It is originally used to
symbolize popular and modern classifiers that have
a well-defined theoretical foundation to provide
some enviable performances /IJ. In this paper, an
admissible kernel function in Banach Space is
proposed as an optima! kernel function for real time
applications. The experimental results are carried
out using benchmark binary datasets which are
taken from UCI Machine Learning Repository and
their performance are evaluated using various
measures like support vector, support vector
percentage, training time and accuracy.