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Comparison of Support Vector Machines With and Without Latent Semantic Analysis for Document Classification

  • Vaibhav Khatavkar
  • Parag Kulkarni
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 808)

Abstract

Document Classification is a key technique in Information Retrieval. Various techniques have been developed for document classification. Every technique aims for higher accuracy and greater speed. Its performance depends on various parameters like algorithms, size, and type of dataset used. Support Vector Machine (SVM) is a prominent technique used for classifying large datasets. This paper attempts to study the effect of Latent Semantic Analysis (LSA) on SVM. LSA is used for dimensionality reduction. The performance of SVM is studied on reduced dataset generated by LSA.

Keywords

Document classification Support vector machine Latent semantic analysis Dimensionality reduction Singular value decomposition Context vector 

Notes

Acknowledgements

We acknowledge the help extended by Mr. Shubham Gatkal, Mr. Sandesh Gupta and Mr. Prathamesh Ingle for experimentation. We would also like to acknowledge the support and encouragements received from the authorities of College  of Engineering, Pune. (COEP).

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Computer Engineering and Information TechnologyCollege of Engineering, PuneShivajinagar, PuneIndia

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