Multiple-criteria decision analysis

Related lectures (32)

Explores binary classification by regression, decision functions, and various cost functions.

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

Explores linear models for classification, logistic regression, SVM, k-NN, and curse of dimensionality.

Explores investment choice using multi-criteria approaches and discusses challenges in criteria selection and scoring methods.

Covers the general framework of optimization and simulation, including deterministic control and the optimization problem.

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

Explores solving strategies for energy system optimization problems and different types of optimization approaches.

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

Covers the basics of support vector machines, logistic regression, decision boundaries, and the k-Nearest Neighbors algorithm.

Covers exercises on optimal decision making, including minimizing costs and optimizing transportation networks.

Covers linear models, logistic regression, decision boundaries, k-NN, and practical applications in authorship attribution and image data analysis.

Explores signed distance, perceptron, logistic regression, cross entropy, and multi-class classification.

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