z-logo
open-access-imgOpen Access
Direct comparison between support vector machine and multinomial naive Bayes algorithms for medical abstract classification
Author(s) -
Stan Matwin,
Vera Sazonova
Publication year - 2012
Publication title -
journal of the american medical informatics association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.614
H-Index - 150
eISSN - 1527-974X
pISSN - 1067-5027
DOI - 10.1136/amiajnl-2012-001072
Subject(s) - support vector machine , computer science , machine learning , naive bayes classifier , artificial intelligence , algorithm , bayesian probability , relevance vector machine , multinomial distribution , task (project management) , bayes' theorem , structured support vector machine , data mining , mathematics , statistics , engineering , systems engineering
In 2011 Matwin et al published a letter to JAMIA entitled ‘Performance of SVM and Bayesian classifiers on the systematic review classification task’.1 This letter continued a discussion on the relative benefits of using support vector machine (SVM) and Bayesian techniques for performing systematic reviews.2–4 In particular, it was suggested that the running time of algorithms must be taken into consideration when comparing their performances as it becomes very important for large datasets.Following up on this idea, we attempted to directly compare the performance of a Bayesian method with the SVM algorithm used by Cohen in his original work.4 The same SVM system (SVMlight5) with the same parameters as was …

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom