Discussion
Which among the following minimizes the upper bound of the generalization error and maximizes the margin between a separating hyperplane and the training data, instead of minimizing the training error ?
Answer: B
The method is the SVM (support vector machine).
An SVM chooses the separating hyperplane with the largest margin to the nearest training points, the support vectors. This follows the structural risk minimisation principle: maximising the margin minimises an upper bound on the generalisation error, rather than just the training error.
Boltzmann machines and Hopfield networks are energy-based recurrent networks used for learning distributions and associative memory, and RBF networks fit radial basis functions to minimise training error.
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