Classification algorithms | Statistical classification
In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data for classification and regression analysis. Developed at AT&T Bell Laboratories by Vladimir Vapnik with colleagues (Boser et al., 1992, Guyon et al., 1993, Cortes and Vapnik, 1995, Vapnik et al., 1997) SVMs are one of the most robust prediction methods, being based on statistical learning frameworks or VC theory proposed by Vapnik (1982, 1995) and Chervonenkis (1974). Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non-probabilistic binary linear classifier (although methods such as Platt scaling exist to use SVM in a probabilistic classification setting). SVM maps training examples to points in space so as to maximise the width of the gap between the two categories. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces. When data are unlabelled, supervised learning is not possible, and an unsupervised learning approach is required, which attempts to find natural clustering of the data to groups, and then map new data to these formed groups. The support vector clustering algorithm, created by Hava Siegelmann and Vladimir Vapnik, applies the statistics of support vectors, developed in the support vector machines algorithm, to categorize unlabeled data. (Wikipedia).
Support Vector Machine Fundamentals - Practical Machine Learning Tutorial with Python p.23
In this tutorial, we cover some more of the fundamentals of the Support Vector Machine. https://pythonprogramming.net https://twitter.com/sentdex https://www.facebook.com/pythonprogramming.net/ https://plus.google.com/+sentdex
From playlist Machine Learning with Python
Support Vector Assertion - Practical Machine Learning Tutorial with Python p.22
In this tutorial, we cover the assertion for the calculation of a support vector within the Support Vector Machine. https://pythonprogramming.net https://twitter.com/sentdex https://www.facebook.com/pythonprogramming.net/ https://plus.google.com/+sentdex
From playlist Machine Learning with Python
Support Vector Machines Part 1 (of 3): Main Ideas!!!
Support Vector Machines are one of the most mysterious methods in Machine Learning. This StatQuest sweeps away the mystery to let know how they work. Part 2: The Polynomial Kernel: https://youtu.be/Toet3EiSFcM Part 3: The Radial (RBF) Kernel: https://youtu.be/Qc5IyLW_hns NOTE: This StatQ
From playlist Support Vector Machines
Support Vector Machine (original paper) | AISC Foundational
Toronto Deep Learning Series https://aisc.a-i.science/events/2019-01-31/ Support Vector Machine The support-vector network is a new learning machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to a ve
From playlist Math and Foundations
Support Vector Machines - Part 1: Introduction
This video is about Support Vector Machines - Part 1: Introduction Abstract: This is a series of videos about Support Vector Machines (SVMs), which will walk through the introduction, the working principle and theory covering a linearly separable case, non-separable case, nonlinear SVM an
From playlist Machine Learning
Support Vector Machines (Intro)
SVM for classification, hard margin and soft margin problems, translating to quadratic programming
From playlist Support Vector Machines
Support Vector Machine Optimization - Practical Machine Learning Tutorial with Python p.24
In this tutorial, we discuss the optimization problem that is the Support Vector Machine, as well as how we intend to solve it ourselves. https://pythonprogramming.net https://twitter.com/sentdex https://www.facebook.com/pythonprogramming.net/ https://plus.google.com/+sentdex
From playlist Machine Learning with Python
Data Science - Part IX - Support Vector Machine
For downloadable versions of these lectures, please go to the following link: http://www.slideshare.net/DerekKane/presentations https://github.com/DerekKane/YouTube-Tutorials This lecture provides an overview of Support Vector Machines in a more relatable and accessible manner. We will g
From playlist Data Science
Support Vector Machine in R | SVM Algorithm Explained with Example | Data Science in R | Simplilearn
This Support Vector Machine in R tutorial video will help you understand what Support Vector Machines are, and the basics of the SVM kernel. You will look at a use case to learn SVM Algorithm with an example where we will classify horses and mules from a given data set using the SVM algori
From playlist Data Science For Beginners | Data Science Tutorial🔥[2022 Updated]
Lecture 07 Support Vector Machines
Machine Learning by Andrew Ng [Coursera] 0701 Optimization objective 0702 Large Margin Intuition 0703 The mathematics behind large margin classification (optional) 0704 Kernels I 0705 Kernels II 0706 Using an SVM
From playlist Machine Learning by Professor Andrew Ng
Support Vector Machine - How Support Vector Machine Works | SVM In Machine Learning | Simplilearn
🔥 Advanced Certificate Program In Data Science: https://www.simplilearn.com/pgp-data-science-certification-bootcamp-program?utm_campaign=MachineLearning-TtKF996oEl8&utm_medium=Descriptionff&utm_source=youtube 🔥 Data Science Bootcamp (US Only): https://www.simplilearn.com/data-science-bootc
From playlist Machine Learning with Python | Complete Machine Learning Tutorial | Simplilearn [2022 Updated]
Support Vector Machines in Python from Start to Finish.
NOTE: You can support StatQuest by purchasing the Jupyter Notebook and Python code seen in this video here: http://statquest.gumroad.com/l/iulnea This webinar was recorded 20200609 at 11:00am (New York Time) NOTE: This StatQuest assumes that you are already familiar with: Support Vector
From playlist Support Vector Machines
Machine Learning by Andrew Ng [Coursera] 07 Support Vector Machines
From playlist Machine Learning by Professor Andrew Ng
Support Vector Machines Introduction
From playlist Machine Learning