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Cooper (p. 23-32)Kunz-Osborne (p. 33-41)Coulmas-Law (p.42-46)Stasio (p. 47-56)Albert-Valette-Florence (p.57-63)Zhang-Rauch (p. 64-70)Alam-Yasin (p. 71-78)Mattare-Monahan-Shah (p. 79-94)Nonis-Hudson-Hunt (p. 95-106) 
JOURNAL OF HIGHER EDUCATION THEORY AND PRACTICE
Online Learning and Active Learning: A Comparative Study of Passive-Aggressive
Algorithm With Support Vector Machine (SVM)
Author(s): K.I Ezukwoke, S.J Zareian
Citation: K.I Ezukwoke, S.J Zareian, (2021) "Online Learning and Active Learning: A Comparative Study of Passive-Aggressive Algorithm With Support Vector Machine (SVM)," Journal of Higher Education Theory and Practice, Vol. 21, ss. 3, pp. 161-171
Article Type: Research paper
Publisher: North American Business Press
Abstract:
Passive aggressive online learning is an extension of Support Vector Machine (SVM) to the context of online learning for binary classification. In this paper we consider the application of the algorithm on anomaly labeling for IJCNN 2001 Neural Network Competition dataset from LibSVM dataset repository1 from Ford Research Laboratory. We also work on an improved version of the online learning algorithm called Active learning and we compare both algorithms to that of SVM (from LibSVM library). We propose different experimental setups for comparing the algorithms.
