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What is SVM, Clustering, and Dimensionality Reduction Techniques about?

The document discusses various concepts related to Support Vector Machines (SVM), including the separating hyperplane, margin, hard and soft margins, and kernel functions. It also covers multi-class classification techniques, constrained optimization, K-means clustering steps, and the Expectation-Maximization algorithm. Additionally, it explains dimensionality reduction techniques like PCA and LDA, as well as density-based clustering methods such as DBSCAN.

Author

harshspam29

Language

EN