# SVCLoss¶

class SVCLoss(**kwargs)[स्रोत]

आधार: KernelLoss

This class provides a kernel loss function for classification tasks by fitting an SVC model from scikit-learn. Given training samples, $$x_{i}$$, with binary labels, $$y_{i}$$, and a kernel, $$K_{θ}$$, parameterized by values, $$θ$$, the loss is defined as:

$SVCLoss = \sum_{i} a_i - 0.5 \sum_{i,j} a_i a_j y_{i} y_{j} K_θ(x_i, x_j)$

where $$a_i$$ are the optimal Lagrange multipliers found by solving the standard SVM quadratic program. Note that the hyper-parameter C for the soft-margin penalty can be specified through the keyword args.

Minimizing this loss over the parameters, $$θ$$, of the kernel is equivalent to maximizing a weighted kernel alignment, which in turn yields the smallest upper bound to the SVM generalization error for a given parameterization.

See https://arxiv.org/abs/2105.03406 for further details.

मापदण्ड:

**kwargs -- Arbitrary keyword arguments to pass to SVC constructor within SVCLoss evaluation.

Methods

 evaluate(parameter_values, quantum_kernel, ...) An abstract method for evaluating the loss of a kernel function on a labeled dataset.