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Centre for Forecasting Lancaster University Management School Lancaster LA1 4YF United Kingdom Tel +44.1524.592991 Fax +44.1524.844885 eMail sven dot crone (at) neural-forecasting dot com | | 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 denotes the input space with *n* objects. Each object belongs to a discrete class ** **and we will refer to a pair 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 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 , where the functions themselves are labeled by the adjustable parameter vector The objective is to modify the free parameters to find a specific learning machine which captures the relationships in the training examples, , 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 . 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 |