Incremental Regression: LWPR

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Related lectures (32)

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Kernel Methods: Representer Theorem and SVM Applications

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

Support Vector Regression: Principles and Optimization

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

Kernel K-means: Iterative Clustering Algorithm

Explores the Kernel K-means iterative clustering algorithm and its influence on cluster density and point proximity.

Kernel Methods: Machine Learning

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

Feature Expansion: Kernels and KNN

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

Feature Expansion and Kernels

Covers feature expansion, kernels, SVM, and nonlinear classification in machine learning.

Introduction to Machine Learning: Supervised Learning

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

Kernel Methods

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

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: RKHS and Kernels

Explores RKHS, positive definite kernels, and the Moore-Aronszajn theorem in kernel methods.