Abstract:
The reason for the creation ofthis system is the need for the
classification ofthe newspaper articles. For the desired purpose we
have collected many newspaper articles from many different
newspapers. The article will be compared with the articles stored in
dataset and ifthe descriptors and key points for the articles matches
with the articles in our dataset, the details of that specific articles
will be sent to the system. Some outputs are going to be available
whichTl show that there is good accuracy ofrecognition of articles.
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Starting from Support Vector Machine (SVM) and its variants
gaining momentum among the Machine Learning community. In
this paper, we present a quantitative analysis between the
established SVM based classifiers on multi-category text
classification problem. Here,. The dataset is first converted into
activities which
are
required format by performing preprocessing
involve tokenization and removing irrelevant data. The feature set is
Term Frequency-Inverse Document Frequency constructed as
matrix, so that representative vectors could be obtained for each
document. Experimentally, and
different models SVM fits best in accuracy, after making models we
ranked those given articles
