Kernel Methods: Representer Theorem and SVM Applications

Related lectures (32)

Page 3 of 4

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

Covers feature expansion, kernels, and K-nearest neighbors, including non-linearity, SVM, and Gaussian kernels.

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

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

Explores incremental learning with LWPR, discussing challenges, synthetic data, real-world applications, and the LWPR algorithm.

Covers nonparametric regression, scatterplot smoothing, kernel methods, and bias-variance tradeoff.

Explores unsupervised learning techniques for reducing dimensions in data, emphasizing PCA, LDA, and Kernel PCA.

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

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

Explores Kernel K-means clustering, interpreting solutions, handling missing data, and dataset selection for machine learning.