In Chapters 3 and 4, we considered linear parametric models for regression and classification in which the form of the mapping y(x,w) from input x to output y is governed by a vector w of adaptive parameters. During the learning phase, a set of training data is used either to obtain a point estimate of the parameter vector or to determine a posterior distribution over this vector. The training data is then discarded, and predictions for new inputs are based purely on the learned parameter vector w. This approach is also used in nonlinear parametric models such as neural networks.
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