Online Learning and Active Learning: A Comparative Study of Passive-Aggressive Algorithm With Support Vector Machine (SVM)
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 reposit...
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| Veröffentlicht in: | Journal of higher education theory and practice 2021, Vol.21 (3), p.161-171 |
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| container_end_page | 171 |
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| container_title | Journal of higher education theory and practice |
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| creator | Ezukwoke, KI Zareian, SJ |
| description | 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. |
| doi_str_mv | 10.33423/jhetp.v21i3.4152 |
| format | Article |
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| source | Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Education Source |
| subjects | Accuracy Active learning Algorithms Classification Comparative Analysis Datasets Distance learning Electronic Learning Experiments Labeling Passive-aggressive behavior Random variables Support vector machines Time series |
| title | Online Learning and Active Learning: A Comparative Study of Passive-Aggressive Algorithm With Support Vector Machine (SVM) |
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