Incremental Regression: LWPR

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Related lectures (32)

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Explores kernel tricks in support vector machines for efficient computation in high-dimensional spaces without explicit transformation.

Neural Network: Random Features and Kernel Regression

Covers random features in neural networks and kernel regression equivalence.

Kernel Ridge Regression: Equivalent Formulations and Representer Theorem

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

Feature Expansion and Kernel Methods

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

Kernel Regression: Weighted Average and Feature Maps

Covers kernel regression and feature maps for data separability.

Support Vector Machine Extensions: SVM, RVM, Transductive SVM

Explores SVM extensions, RVM, Transductive SVM, and support vector clustering in advanced machine learning.

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 Regression: K-nearest Neighbors

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

Machine Learning Fundamentals

Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.

Support Vector Regression: Nu-SVR and RVR

Explores advanced topics in machine learning, focusing on SVR extensions and hyperparameter optimization, including Nu-SVR and RVR.