Multiclass classification

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Related lectures (29)

Vapnik-Chervonenkis dimension

Covers learning bounds, complexities, growth function, shattering, and VC dimension in binary classifiers.

Support Vector Machine Overview

Gives an overview of Support Vector Machines, comparing advantages and disadvantages of SVM with other classifiers.

Linear Regression: Basics

Covers the basics of linear regression, binary and multi-class classification, and evaluation metrics.

Linear Models: Part 2

Covers linear models, binary and multi-class classification, and logistic regression with practical examples.

Support Vector Machines: Parameters, Solutions, and Boundaries

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

Multiclass SVM

Covers the use of Support Vector Machines for multi-class classification and the importance of support vectors in tightening classification boundaries.

Support Vector Machine and Logistic Regression

Explains support vector machine and logistic regression for classification tasks, emphasizing margin maximization and risk minimization.

Support Vector Machines: Soft Margin

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

Generalized Linear Regression

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

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