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Data driven methods from computational intelligence share the common approach of learning machines in classification for data mining Let all relevant and measurable attributes of an object, e.g. a customer, be combined in a vector x and the set <![if !vml]><![endif]> denotes the input space with n objects. Each object belongs to a discrete class <![if !vml]><![endif]> and we will refer to a pair <![if !vml]><![endif]> as an example of our classification problem. Presuming that it is impossible to model the relationship between attribute vector x and class membership y directly, either because it is unknown, to complex or the data is corrupted by noise, and that a sufficient large set of examples <![if !vml]><![endif]> is available, we can incorporate a machine to learn the mapping between x and y. The learning machine is actually defined by a set of possible mappings <![if !vml]><![endif]>, where the functions <![if !vml]><![endif]> themselves are labeled by the adjustable parameter vector <![if !vml]><![endif]> The objective is to modify the free parameters <![if !vml]><![endif]> to find a specific learning machine which captures the relationships in the training examples, <![if !vml]><![endif]>, incrementally minimizing a given objective function and generalizing the problem structure within to allow correct estimation of unseen objects on the basis of their attribute values <![if !vml]><![endif]>.
For most questions of parameterisation only rules of thumb are known. Answers that are valid for all kinds of problems cannot be given – each problem needs its own. Therefore, knowledge not only in the field of soft computing but also in the problem domain is necessary. Following, we outline the specific modelling-properties for classification for alternative network paradigms. For a comprehensive discussion readers are referred to
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