I encountered a problem when I was doing quantum support vector machine experiments: when I was doing quantum kernel alignment, I saw that the example given by qiskit was SVC_Loss: https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.utils.loss_functions.SVCLoss.html#qiskit_machine_learning.utils.loss_functions.SVCLoss

Its core code is: SVCLoss = \sum_{i} a_i - 0.5 \sum_{i,j} a_i a_j y_{i} y_{j} K_θ(x_i, x_j)

svc_loss = np.sum(np.abs(dual_coefs)) - (0.5 * (dual_coefs.T @ kmatrix @ dual_coefs))

I wonder how the loss function of svr looks like: SVRLoss = \sum_{i} a_i + 0.5 \sum_{i,j} a_i a_j y_{i} y_{j} K_θ(x_i, x_j)

svr_loss = np.sum(np.abs(dual_coefs)) + (0.5 * (dual_coefs.T @ kmatrix @ dual_coefs))

is that so?

Thanks for your prompt answer, it was very helpful to me!



Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.