The number of learning iterations over the training data, which is the only adjustable parameter of the algorithm, was determined by cross-validation.
in order to overcome the limitations of a linear perceptron, we introduce a feature map Φ: that maps a feature vector x into a higher dimensional feature space consisting of all unordered feature pairs: we expand the original representation in the input space with a feature map that generates all second-order feature combinations from each observation.