Let The Optimization Problem Using Soft Svm Is Minimize A #392

Let the optimization problem using Soft SVM is minimize a function and the update rule of SGD is w^(t+1) = – (\(\frac {1}{\lambda t}\) ∑\(^t _{j=1}\)v_j) then V_j is the sub gradient of the loss function.

This multiple choice question (MCQ) is related to the book/course gs gs126 Neural Networks. It can also be found in gs gs126 Support Vector Machines - Implementing Soft SVM with SGD - Quiz No.1.