Support Vector Regression: Principles and Optimization

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Explores Ridge and SVR regression, emphasizing kernel tricks for non-linear regression.

Covers linear and weighted regression, optimal parameters, local solutions, SVR application, and regression techniques' sensitivity.

Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.

Covers the probabilistic model for linear regression and the importance of regularization techniques.

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

Explores kernel methods in machine learning, emphasizing their application in regression tasks and the prevention of overfitting.

Introduces supervised learning, covering classification, regression, model optimization, overfitting, and kernel methods.

Introduces linear regression, covering its fundamentals, applications, and evaluation metrics in machine learning.

Explores generative and discriminative classification algorithms, emphasizing their applications and differences in machine learning tasks.

Covers linear regression, weighted regression, locally weighted regression, support vector regression, noise handling, and eye mapping using SVR.