Experimental Results for (Classical) Support Vector Machine (SVM) on NSL-KDD Dataset Dataset
Preface
Experimental Results
- The experimental results...
Note(s):
- For these results, it was used the Support Vector Classifier (SVC) from SciKit-Learn library. For more information, see the following link:
Footnote(s):
- ¥ - The 'rbf' kernel stands for Radial Basis Function (RBF) kernel.
- § - This hyperparameter represents the Regularization Factor/Parameter.
- † - This hyperparameter represents the Polynomial Degree for 'poly' kernel.
- ‡ - This hyperparameter represents the (non-linear) Kernel Coefficient for 'rbf', 'poly', and 'sigmoid' kernels. This hyperparameter is also denoted by the Greek letter γ.
- ¶ - The higher Accuracy values are highlighted in green and the lowest Accuracy values are highlighted in red.
License
Categories:
experimental-results
Tags:
artificial-intelligence, computer-science, classical-support-vector-machine, support-vector-machine, classical-machine-learning, machine-learning, supervised-learning, training, classification, iris-dataset, and intermediate