Positive-definite kernel

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Related lectures (32)

Page 2 of 4

Feature Maps and Kernels

Covers feature maps, Representer theorem, kernels, and RKHS in machine learning.

Kernel Methods: Machine Learning

Covers Kernel Methods in Machine Learning, focusing on overfitting, model selection, cross-validation, regularization, kernel functions, and SVM.

Kernel Methods: Efficient Dot Product Computation

Demonstrates efficient dot product computation and introduces nonlinear boundaries in the original space.

Feature Selection, Kernel Regression, Neural Networks Playground

Covers feature selection, kernel regression, and neural networks through exercises.

Kernel K-means: Advanced Machine Learning

Introduces Kernel K-means, extending K-means to create non-linear separations of data points.

Kernel Methods: Neural Networks

Covers the fundamentals of neural networks, focusing on RBF kernels and SVM.

Support Vector Machines: Kernel SVM

Explores non-linear SVM using kernels for data separation in higher-dimensional spaces, optimizing training with kernels to avoid explicit transformations.

Kernel Methods and Regression

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

Support Vector Machines: Theory and Optimization

Delves into the theory and optimization of Support Vector Machines, including Mercer's Theorem and Lagrange Duality.

Kernel Methods in Machine Learning: Kernel Regression and SVM

Discusses kernel methods in machine learning, focusing on kernel regression and support vector machines, including their formulations and applications.