(21st-May-2020)
Given a representation and a bias, the problem of learning can be reduced to one of search.
Learning is a search through the space of possible representations, trying to find the representation or representations that best fits the data given the bias. Unfortunately, the search spaces are typically prohibitively large for systematic search, except for the simplest of examples. Nearly all of the search techniques used in machine learning can be seen as forms of local search through a space of representations. The definition of the learning algorithm then becomes one of defining the search space, the evaluation function, and the search method.
Noise
In most real-world situations, the data are not perfect. Noise exists in the data (some of the features have been assigned the wrong value), there are inadequate features (the features given do not predict the classification), and often there are examples with missing features. One of the important properties of a learning algorithm is its ability to handle noisy data in all of its forms.
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