Svm in machine learning: Support Vector Machine (SVM) is a supervised

Support Vector Machine (SVM) is a supervised machine learning algorithm used for classification and regression tasks. It tries to find the best boundary known as hyperplane that separates different classes in the data. Learn how SVMs are used for classification and regression problems, and how they find a hyperplane that maximizes the margin between classes. See examples of SVM implementation in Python using sklearn library and kernel trick. Support Vector Machine , or SVM , is one of the most popular Supervised Learning algorithms used for Classification, Regression, and anomaly detection problems. Learn more on Scaler Topics. Definition ‘Support Vector Machine is a system for efficiently training linear learning machines in kernel-induced feature spaces, while respecting the insights of generalisation theory and exploiting optimisation theory.’ AN INTRODUCTION TO SUPPORT VECTOR MACHINES (and other kernel-based learning methods) N. Cristianini and J. Shawe-Taylor Cambridge University Press