Hyperparameters. The SVDD has two hyperparameters: ν and γ. The hyperparameter ν in the objective controls the penalty for slack granted to the constraints and makes a trade off between the minimization of the radius of the hypersphere and the number of training errors training vectors outside the hypersphere) allowed . A larger ν will allow more training errors and give a smaller hypersphere. A smaller ν forces more of the training vectors to lie inside the hypersphere but results in a larger hypersphere. The hyperparameter γ belongs to the kernel function. For the RBF kernel in eqn. 3, γ is a length-scale. A large γ yields an optimal hypersphere in kernel space that translates to a smooth decision boundary in original sensor response space while a small γ produces a more wiggly decision boundary. A larger γ and larger ν may help prevent overfitting to the training data. See Fig. .