Soft Margin Classification – Noisy data
In previous section, we studied about Kernel – Non Linear Classifier
Noisy data
- What if there is some noise in the data.
- What id the overall data can be classified perfectly except few points.
- How to find the hyperplane when few points are on the wrong side.
Soft Margin Classification – Noisy data
- The non-separable cases can be solved by allowing a slack variable(x) for the point on the wrong side.
- We are allowing some errors while building the classifier
- In SVM optimization problem we are initially adding some error and then finding the hyperplane
- SVM will find the maximum margin classifier allowing some minimum error due to noise.
- Hard Margin -Classifying all data points correctly
- Soft margin – Allowing some error
SVM Validation
- SVM doesn’t give us the probability, it directly gives us the resultant classes.
- Usual methods of validation like sensitivity, specificity, cross validation, ROC and AUC are the validation methods.
- The next post is about Digit Recognition using SVM.
