Given The Soft Svm Optimization Problem And The Update Rule Of #393
Given the Soft SVM optimization problem and the update rule of SGD w<sup>(t+1)</sup> = – (\(\frac {1}{\lambda t}\) ∑\(^t _{j=1}\)v<sub>j</sub>). For the hinge loss, given an example (x, y), it can choose V<sub>j</sub> to be one if y (w<sup>(J)</sup>, x) ≥ 1.
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.
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