Support Vector Machines: Definition and Separation Hyperplane

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Covers the concept of kernel regression and making data linearly separable by adding features and using local methods.

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

Explores Support Vector Machines with a focus on soft margin and multiclass classification using binary classifiers.

Covers the basics of support vector machines, logistic regression, decision boundaries, and the k-Nearest Neighbors algorithm.

Covers the history and applications of SVM, as well as the construction of linear classifiers and the concept of classifier margin.

Covers statistical inference, machine learning, SVMs for spam classification, email preprocessing, and feature extraction.

Explores SVM model storage, memory usage, training time complexity, and energy consumption estimation.

Explores Kernel Ridge Regression, the Representer Theorem, and the Kernel Trick in machine learning.

Explores Support Vector Machines in machine learning, discussing SVM, support vectors, uniqueness of solutions, and multi-class SVM.

Explores maximizing margins for better classification using support vector machines and the importance of choosing the right parameter.