Two forms of the data-driven modelling are regression and classification. Based on some measured variables, both of them predict the value of one or more variables we are interested in. In case of regression there are continuous or ordered variables, in case of classification there are discrete or nominal variables needed to be predicted. Classification is also called supervised learning because the labels of the samples are known beforehand. This is the main difference between classification and clustering. The later is unsupervised learning since clusters want to be determined and the labels of the data points are not known.


Membership Function Fuzzy Model Fuzzy Cluster Genetic Algo Gaussian Membership Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Birkhäuser Verlag AG 2007

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