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Support vector machines or support vector networks are models based on supervised learning models that are primarily used for classification and regression analysis. Support vector machine or SVM is a robust and highly efficient prediction method with a firm foundation in statistical methods It was developed at AT&T Bell Laboratories by Vladimir Vapnik and his fellow workers.
SVM works like any other machine learning algorithm. An SVM is fed a set of training examples with each marked as belonging to one of two classes; its training algorithm builds a model that assigns new examples to one class or the other, so in essence, it is a non-probabilistic binary linear classifier. Geometrically, an SVM maps training examples to points in space and then tries to maximize the width of the gap between the two categories. For prediction, the input value is mapped into that same space and predicted to belong to a class based on which side of the margin they fall. In simple terms, an SVM classifies the data into classes by creating a line or a hyperplane which separates the data into classes.
When compared to more advanced systems like deep neural networks, SVM holds two main advantages i.e., SVM has higher speed and has better performance even with a small sample set.
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Data is the soul of Machine Learning, and there are specific methods to deal with it efficiently. This module first introduces Machine Learning and talks about the mathematical procedures involved. You will learn about supervised and unsupervised learning, Data Science Machine Learning steps, linea…
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MIT 18.650 Statistics for Applications, Fall 2016 View the complete course: http://ocw.mit.edu/18-650F16 Instructor: Philippe Rigollet This course offers an in-depth the theoretical foundations for statistical methods that are useful in many applications. The goal is to understand the role of mathe…
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-Learn more and enroll in the program: https://online.stanford.edu/programs/generative-ai-technology-business-and-society-program Recent advancements in generative AI are reshaping industries, pushing technological boundaries, and revolutionizing creative processes. In this fast-changing landscape…
For more information about Stanford's Artificial Intelligence programs visit: https://stanford.io/ai Delve into the exciting world of Artificial Intelligence with insights and research from the world's top experts. This playlist offers a comprehensive journey, from foundational machine learning con…
Led by Andrew Ng, this course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (cluster…
View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Patrick Winston In these lectures, Prof. Patrick Winston introduces the 6.034 material from a conceptual, big-picture perspective. Topics include reasoning, search, constraints, learning, representations, architectures, and probabili…
