Data Science: Support Vector Machines (Definitions)

"A supervised machine learning classification approach with the objective to find the hyperplane maximizing the minimum distance between the plane and the training data points." (Xiaoyan Yu et al, "Automatic Syllabus Classification Using Support Vector Machines", 2009)

"Support vector machines [SVM] is a methodology used for classification and regression. SVMs select a small number of critical boundary instances called support vectors from each class and build a linear discriminant function that separates them as widely as possible." (Yorgos Goletsis et al, "Bankruptcy Prediction through Artificial Intelligence", 2009)

"SVM is a data mining method useful for classification problems. It uses training data and kernel functions to build a model that can appropriately predict the class of an unclassified observation." (Indranil Bose, "Data Mining in Tourism", 2009)

"A modeling technique that assigns points to classes based on the assignment of previous points, and then determines the gap dividing the classes where the gap is furthest from points in both classes." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"A machine-learning technique that classifies objects. The method starts with a training set consisting of two classes of objects as input. The SVA computes a hyperplane, in a multidimensional space, that separates objects of the two classes. The dimension of the hyperspace is determined by the number of dimensions or attributes associated with the objects. Additional objects (i.e., test set objects) are assigned membership in one class or the other, depending on which side of the hyperplane they reside." (Jules H Berman, "Principles of Big Data: Preparing, Sharing, and Analyzing Complex Information", 2013)

"A machine learning algorithm that works with labeled training data and outputs results to an optimal hyperplane. A hyperplane is a subspace of the dimension minus one (that is, a line in a plane)." (Judith S Hurwitz, "Cognitive Computing and Big Data Analytics", 2015)

"A classification algorithm that finds the hyperplane dividing the training data into given classes. This division by the hyperplane is then used to classify the data further." (David Natingga, "Data Science Algorithms in a Week" 2nd Ed., 2018)

"Machine learning techniques that are used to make predictions of continuous variables and classifications of categorical variables based on patterns and relationships in a set of training data for which the values of predictors and outcomes for all cases are known." (Jonathan Ferrar et al, "The Power of People: Learn How Successful Organizations Use Workforce Analytics To Improve Business Performance", 2017)

"It is a supervised machine learning tool utilized for data analysis, regression, and classification." (Shradha Verma, "Deep Learning-Based Mobile Application for Plant Disease Diagnosis", 2019)

"It is a supervised learning algorithm in ML used for problems in both classification and regression. This uses a technique called the kernel trick to transform the data and then determines an optimal limit between the possible outputs, based on those transformations." (Mehmet A Cifci, "Optimizing WSNs for CPS Using Machine Learning Techniques", 2021)

"Support Vector Machines (SVM) are supervised machine learning algorithms used for classification and regression analysis. Employed in classification analysis, support vector machines can carry out text categorization, image classification, and handwriting recognition." (Accenture)