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Software . 2017
Data sources: Datacite
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Software . 2017
Data sources: ZENODO
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Software . 2017
Data sources: Datacite
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SVM-Classification: Multiclass Linear SVC SVM classification of text files into categories

integration_instructionsResearch softwarekeyboard_double_arrow_right Software 23 Jun 2017Publisher:Zenodo
Authors: , Jami;
Code repository: https://github.com/jamalarmel/SVM-Classification/tree/v1.0

doi: 10.5281/zenodo.833655 , 10.5281/zenodo.817592

SVM-Classification: Multiclass Linear SVC SVM classification of text files into categories

Abstract

Classification of text files into categories can be used to develop a content based skill set detection application to quickly suggest to other team members who has the relevant knowledge in the team. The project's focus is to develop a classifier based on the supervised learning technique Linear SVC to classify conversations into a set of 3 pre-defined and mutually exclusive categories: Java, Scala and Python.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
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