Decision boundary

Related lectures (23)

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Explores SVM parameters, solutions, and decision boundaries, including the uniqueness of solutions and the impact of kernel width.

Explores Support Vector Machines theory, parameters, uniqueness, and applications in machine learning.

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

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

Explores Support Vector Machines, maximizing margin for robust classification and the transition to soft SVM for non-linearly separable data.

Covers linear models for classification, focusing on logistic regression and support vector machines.

Explores the extension of linear classifiers to handle multiclass problems and compares their performance on various datasets.

Covers supervised learning, classification, regression, decision boundaries, overfitting, Perceptron, SVM, and logistic regression.

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

Explores linear models for classification, including logistic regression, decision boundaries, and support vector machines.