Research for SVM Classification
using MATLAB at QUAD SOFTWARES Vadapalani
SVM is a useful
technique for data classification. Even though it’s considered that Neural
Networks are easier to use than this, however, sometimes unsatisfactory results
are obtained. A classification task
usually involves with training and testing data which consist of some data
instances. Each instance in the training set contains one target values and
several attributes. The goal of SVM is to produce a model which predicts target
value of data instances in the testing set which are given only the attributes.
in SVM is an example
of Supervised Learning. Known
labels help indicate whether the system is performing in a right way or not.
This information points to a desired response, validating the accuracy of the
system, or be used to help the system learn to act correctly. A step in SVM
classification involves identification as which are intimately connected to the
known classes. This is called feature
selection or feature extraction.
Feature selection and SVM classification together have a use even when
prediction of unknown samples is not necessary. They can be used to identify
key sets which are involved in whatever processes distinguish the classes.
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