Support Vector Machines: Hyperparameters and V-SVM

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (31)

Page 3 of 4

Optimization Techniques: Stochastic Gradient Descent and Beyond

Discusses optimization techniques in machine learning, focusing on stochastic gradient descent and its applications in constrained and non-convex problems.

SVM - Principle: Linear Classifiers

Covers the history and applications of SVM, as well as the construction of linear classifiers and the concept of classifier margin.

Support Vector Regression: Principles and Optimization

Covers Support Vector Regression principles, optimization, and hyperparameters' influence on the fit.

Advanced Machine Learning: Brief review of C-SVM

Covers clustering, classification, and Support Vector Machine principles, applications, and optimization, including non-linear classification and Gaussian kernel effects.

Introduction to Machine Learning: Supervised Learning

Introduces supervised learning, covering classification, regression, model optimization, overfitting, and kernel methods.

Robust Optimization: Radiation Therapy & Support Vector Machines

Explores robust optimization in radiation therapy and support vector machines.

Duality: Duality in Linear Optimization

Covers the concept of linear optimization and the duality relationship between primal and dual problems.

Support Vector Machine Overview

Gives an overview of Support Vector Machines, comparing advantages and disadvantages of SVM with other classifiers.

Introduction to Optimization

Covers the basics of optimization, including historical perspectives, mathematical formulations, and practical applications in decision-making problems.

Semi-Definite Programming

Covers semi-definite programming and optimization over positive semidefinite cones.