May 2018
Intermediate to advanced
576 pages
14h 42m
English
Content preview from Mastering Machine Learning Algorithms
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Hinge cost function
This cost function is adopted by SVM, where the goal is to maximize the distance between the separation boundaries (where the support vector lies). It's analytic expression is:
Contrary to the other examples, this cost function is not optimized using classic stochastic gradient descent methods, because it's not differentiable at all points where:
For this reason, SVM algorithms are optimized using quadratic programming techniques.
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