Text Categorization with Fractional Gradient Descent Support Vector Machine

DOI

10.2991/assehr.k.200303.038How to use a DOI?

Keywords

Support Vector Machine, text categorization, Fractional Gradient Descent

Abstract

Text documents on the web are an incredible resource including one example of big data, large size and so many variations that it becomes difficult for humans to choose meaningful information without the help of a computer. Text categorization job is to automatically classify text documents into standards class based on their content. The objective of this research is to implement a classifier with optimization based on the Fractional Gradient Descent in text classification. In our research, we propose using the Fractional Gradient Descent to optimize the SVM classifier so that it can increase the speed of training data. We explore a batch of different training data to compare the speed of the UCI ML text dataset training process with the SVM-SGD and SVM-FGD classifiers. This research concludes that using SVM-FGD will optimize the training time for text dataset in the activity of data classification.

Copyright

© 2020, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Dian Puspita Hapsari
AU  - Imam Utoyo
AU  - Santi Wulan Purnami
PY  - 2020
DA  - 2020/03/06
TI  - Text Categorization with Fractional Gradient Descent Support Vector Machine
BT  - Proceedings of the 1st International Multidisciplinary Conference on Education, Technology, and Engineering (IMCETE 2019)
PB  - Atlantis Press
SP  - 157
EP  - 160
SN  - 2352-5398
UR  - https://doi.org/10.2991/assehr.k.200303.038
DO  - 10.2991/assehr.k.200303.038
ID  - Hapsari2020
ER  -