What is cost sensitive SVM?
A new procedure for learning cost-sensitive SVM(CS-SVM) classifiers is proposed. The SVM hinge loss is extended to the cost sensitive setting, and the CS-SVM is derived as the minimizer of the associated risk. The proposed algorithm is also extended to cost-sensitive learning with example dependent costs.
What is cost in SVM?
The cost parameter decides how much an SVM should be allowed to “bend” with the data. For a low cost, you aim for a smooth decision surface and for a higher cost, you aim to classify more points correctly. It is also simply referred to as the cost of misclassification.
What is gamma value in SVM?
Intuitively, the gamma parameter defines how far the influence of a single training example reaches, with low values meaning ‘far’ and high values meaning ‘close’. The gamma parameters can be seen as the inverse of the radius of influence of samples selected by the model as support vectors.
What does the C parameter mean in SVM?
An this is where the C parameter enters the stage. The core idea is simple: we modify the optimization problem to optimize both the fit of the line to data and penalizing the amount of samples inside the margin at the same time, where C defines the weight of how much samples inside the margin contribute to the overall error.
How to find a better model in SVM?
To find a better model, I guess we have to just set a large threshold and do cross-validation. Take it this way. C parameter in SVM is Penalty parameter of the error term . You can consider it as the degree of correct classification that the algorithm has to meet or the degree of optimization the the SVM has to meet.
What is the influence of C in SVMs with?
Tuning C correctly is a vital step in best practice in the use of SVMs, as structural risk minimisation (the key principle behind the basic approach) is party implemented via the tuning of C. The parameter C enforces an upper bound on the norm of the weights, which means that there is a nested set of hypothesis classes indexed by C.
When to misclassify C in a SVM?
For very tiny values of C, you should get misclassified examples, often even if your training data is linearly separable. In a SVM you are searching for two things: a hyperplane with the largest minimum margin, and a hyperplane that correctly separates as many instances as possible.