Hinge loss

Related lectures (30)

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Covers classification problems and various loss functions used in machine learning.

Explores generalized linear regression, logistic regression, and multiclass classification in machine learning.

Covers the concept of multiclass classification and the challenges of linearly separating data with multiple classes.

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

Explores SVMs, feature maps, and the importance of finding the maximum margin solution for classification problems.

Explores Bayesian inference, multiclass classification, logistic regression, and linear regression inference.

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

Covers SVM and multiclass classification using one-vs-all and one-vs-one approaches.

Covers the derivation of Linear Support Vector Machine (SVM) and the Karush-Kuhn-Tucker (KKT) conditions.

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