Least-squares support vector machine

Method of least squares | Lecture 13 | Vector Calculus for Engineers

The method of least-squares is derived for fitting a line to experimental points by setting partial derivatives equal to zero. Join me on Coursera: https://www.coursera.org/learn/vector-calculus-engineers Lecture notes at http://www.math.ust.hk/~machas/vector-calculus-for-engineers.pdf

From playlist Vector Calculus for Engineers

Least squares method for simple linear regression

In this video I show you how to derive the equations for the coefficients of the simple linear regression line. The least squares method for the simple linear regression line, requires the calculation of the intercept and the slope, commonly written as beta-sub-zero and beta-sub-one. Deriv

From playlist Machine learning

Solving the Least Squares Problem Using Gradients

From playlist Least-Squares Problems, Linear Algebra, and Machine Learning

Solving the Least-Squares Problem Using Geometry

From playlist Least-Squares Problems, Linear Algebra, and 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 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

StatsLearning Lect12 R SVM A 111213

From playlist ISLR Chapter 9: Support Vector Machines

Support Vector Machines Part 3: The Radial (RBF) Kernel (Part 3 of 3)

Support Vector Machines use kernel functions to do all the hard work and this StatQuest dives deep into one of the most popular: The Radial (RBF) Kernel. We talk about the parameter values, how they calculate high-dimensional coordinates and then we'll figure out, step-by-step, how the Rad

From playlist Support Vector Machines

Andreas Artemiou

From playlist Contributed talks One World Symposium 2020

Determine a Least Squares Solutions to Ax=b

This video explains how to determine a least-squares solutions to Ax=b that has no solution.

From playlist Least Squares Solutions

Support Vector Machines (3): Kernels

The kernel trick in the SVM dual; examples of kernels; kernel form for least-squares regression

From playlist cs273a

Lecture 8 | Machine Learning (Stanford)

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng continues his lecture about support vector machines, including soft margin optimization and kernels. This course provides a broad introduction to machine learning and

From playlist Lecture Collection | Machine Learning

Stanley Osher: "Linearized Bregman Algorithm for L1-regularized Logistic Regression"

Graduate Summer School 2012: Deep Learning, Feature Learning "Linearized Bregman Algorithm for L1-regularized Logistic Regression" Stanley Osher, UCLA Institute for Pure and Applied Mathematics, UCLA July 20, 2012 For more information: https://www.ipam.ucla.edu/programs/summer-schools/g

From playlist GSS2012: Deep Learning, Feature Learning

Complete Statistical Theory of Learning (Vladimir Vapnik) | MIT Deep Learning Series

Lecture by Vladimir Vapnik in January 2020, part of the MIT Deep Learning Lecture Series. Slides: http://bit.ly/2ORVofC Associated podcast conversation: https://www.youtube.com/watch?v=bQa7hpUpMzM Series website: https://deeplearning.mit.edu Playlist: http://bit.ly/deep-learning-playlist

From playlist AI talks

Lecture 14 - Support Vector Machines

Support Vector Machines - One of the most successful learning algorithms; getting a complex model at the price of a simple one. Lecture 14 of 18 of Caltech's Machine Learning Course - CS 156 by Professor Yaser Abu-Mostafa. View course materials in iTunes U Course App - https://itunes.apple

From playlist Machine Learning Course - CS 156

Stephen Wright: " Some Relevant Topics in Optimization, Pt. 1"

Graduate Summer School 2012: Deep Learning Feature Learning "Some Relevant Topics in Optimization, Pt. 1" Stephen Wright, University of Wisconsin-Madison Institute for Pure and Applied Mathematics, UCLA July 16, 2012 For more information: https://www.ipam.ucla.edu/programs/summer-school

From playlist GSS2012: Deep Learning, Feature Learning

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

Simon Lacoste-Julien: Apprentissage statistique et big data : notions de base pour l'analyse [...]

Find this video and other talks given by worldwide mathematicians on CIRM's Audiovisual Mathematics Library: http://library.cirm-math.fr. And discover all its functionalities: - Chapter markers and keywords to watch the parts of your choice in the video - Videos enriched with abstracts, b

From playlist Mathematical Aspects of Computer Science

StatsLearning Chapter 9 - part 1

From playlist ISLR Chapter 9: Support Vector Machines