Support Vector Machines
Master of Science in Computer Science · University · Machine Learning
This unit introduces the fundamental concepts of Support Vector Machines (SVMs), a powerful supervised learning algorithm. Students will explore SVM models, kernel functions, and their applications in classification and regression tasks.
Zum Üben gibt es 52 Aufgaben. Jede Antwort bekommt sofort eine Rückmeldung, kostenlos und ohne Anmeldung.
Das gehört dazu
- Linear SVM
- Nonlinear SVM
- Kernel Functions (Linear
- Polynomial
- RBF
- Sigmoid)
- Hyperplane and Support Vectors
- Margin and Soft Margin
- Overfitting and Regularization
- SVM for Classification
- SVM for Regression
- SVM Parameter Tuning
Stichworte
- SVM
- Kernel Functions
- Classification
- Regression
- Hyperplane
- Support Vectors
- Margin
- Overfitting
- Regularization