Support Vector Machines: Soft Margin

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

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

Explores Support Vector Machines, covering hard-margin, soft-margin, hinge loss, risks comparison, and the quadratic hinge loss.

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

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

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

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

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

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

Covers linear models for classification, including SVM, decision boundaries, support vectors, and Lagrange duality.