Multiclass SVM

Related lectures (29)

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

Explores the Nearest Neighbor Rules, k-NN algorithm challenges, Bayes classifier, and k-means algorithm for clustering.

Covers linear models for multi-class classification, focusing on logistic regression and evaluation metrics.

Explores supervised learning in financial econometrics, emphasizing classification algorithms like Naive Bayes and Logistic Regression.

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

Introduces logistic regression for binary classification and explores multiclass classification using OvA and OvO strategies.

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

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

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

Covers Gaussian Naive Bayes, K-nearest neighbors, and hyperparameter tuning in machine learning.