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Support Vector Machines for Predicting Electrical Faults | ||
| Engineering and Technology Journal | ||
| Article 4, Volume 32, Issue 8, 2014, Pages 1931-1941 PDF (273.32 K) | ||
| DOI: 10.30684/etj.32.8A4 | ||
| Authors | ||
| Tarik Rashid; Salar J. Abdulhameed | ||
| Abstract | ||
| Support vector machines (SVMs) are a non-probabilistic binary linear classifier in machine learning techniques and are supervised learning algorithms that classify, predict, recognise and analyse patterns. This technique was developed in early 1990s.Training algorithms of support vector machines help build a model that assigns new examples into one class or the other when a set of training examples is recycled in the training process. This feature in SVM has attracted many of researchers to develop SVM methods and their applications. In this paper work support vector machines are used to tackle electrical faults in single phase circuits. Support vectors machines are evaluated against Simple Linear Regression techniques. Support vector machines outperformed Simple Linear Regression techniques. | ||
| Keywords | ||
| Support Vector Machines; A simple Linear Regression technique; Electrical Fault Perdition | ||
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