Learning the Kernel: Convex Optimization

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Related lectures (30)

Page 2 of 3

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

Kernel Ridge Regression: Equivalent Formulations and Representer Theorem

Explores Kernel Ridge Regression, equivalent formulations, Representer Theorem, Kernel trick, and predicting with kernels.

Learning the Kernel Solution: MGT-418 Tutorial

Explores learning the kernel solution in convex optimization, focusing on predicting outputs using a linear classifier and addressing possible numerical issues.

Max-Margin Classifiers

Explores maximizing margins for better classification using support vector machines and the importance of choosing the right parameter.

Kernel Methods: Machine Learning

Explores kernel methods in machine learning, emphasizing their application in regression tasks and the prevention of overfitting.

Feature Expansion and Kernel Methods

Explores feature expansion, kernel methods, SVM, and nonlinear classification in machine learning.

Kernel Methods and Regression

Covers kernel methods, kernel regression, RBF kernel, and SVM for classification.

Support Vector Machines: Maximizing Margin

Explores Support Vector Machines, maximizing margin for robust classification and the transition to soft SVM for non-linearly separable data.

Nonparametric Regression: Kernel-Based Estimation

Covers nonparametric regression using kernel-based estimation techniques to model complex relationships between variables.

Kernel Regression

Covers the concept of kernel regression and making data linearly separable by adding features and using local methods.