Support Vector Machines: Kernel SVM

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (32)

Page 2 of 4

Mercer Theorem and Kernels

Explores the Mercer Theorem, Kernels, and their role in machine learning applications.

Kernel Methods: SVM and Regression

Introduces kernel methods like SVM and regression, covering concepts such as margin, support vector machine, curse of dimensionality, and Gaussian process regression.

Kernel Methods

Covers overfitting, model selection, validation methods, kernel functions, and SVM concepts.

Kernel Methods and Regression

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

Learning the Kernel: Convex Optimization

Explores learning the kernel function in convex optimization, focusing on predicting outputs using a linear classifier and selecting optimal kernel functions through cross-validation.

Kernel Methods: Representer Theorem and SVM Applications

Explores the Representer Theorem, SVM applications, smoothness measurement, kernel combinations, and scalability in kernel methods.

Kernel Regression: K-nearest Neighbors

Covers the concept of kernel regression and K-nearest neighbors for making data linearly separable.

Redescription SVM and Hilbert Spaces

Explores redescription SVM and Hilbert spaces for solving nonlinear problems using inner product spaces and separable Hilbert spaces.

Kernel Ridge Regression: Equivalent Formulations and Representer Theorem

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

Support Vector Machines: Basics and Applications

Covers the basics of Support Vector Machines, including linear separability, hyperplanes, margins, and non-linear SVM with kernels.