Abstract
Machine Learning means “learning through Machines”. We make machine learn and predict the behavior in order to find a solution to a problem. Machine is set to make various predictions based on the learning mechanisms that have been incorporated in them. There are various techniques through which machines can learn. Learning can be supervised, Unsupervised or Semi-supervised. Under these learning schemes we have various classifications. Under Supervised learning we have Classification and Regression. Classification works on continuous values and Regression works on discrete values. Support Vector Machine is an efficient classifier which are mostly sort of linear and comes under supervised method of learning. SVM also find its application in real life for Face Detection, Bioinformatics, Handwriting recognition, image classification and many others.
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Pathak, R. (2020). Support Vector Machines: Introduction and the Dual Formulation. In: Gunjan, V., Senatore, S., Kumar, A., Gao, XZ., Merugu, S. (eds) Advances in Cybernetics, Cognition, and Machine Learning for Communication Technologies. Lecture Notes in Electrical Engineering, vol 643. Springer, Singapore. https://doi.org/10.1007/978-981-15-3125-5_57
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DOI: https://doi.org/10.1007/978-981-15-3125-5_57
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