Supervised and unsupervised pattern recognition

Mathematically, this means that one needs to assign portions of an 8-dimensionaI space to the three classes. A new sample is then assigned to the class which occupies the portion of space in which the sample is located. Supervised pattern recognition is distinct from unsupervised pattern recognition. In the latter one applies essentially clustering methods (Chapter 30) to classify objects into classes that are not known beforehand. In supervised pattern recognition, one knows the classes and has to decide in which of those an object should be classified. 

Supervised pattern recognition techniques essentially consist of the following steps. 

Selection of a training or learning set. This consists of objects of known classification for which a certain number of variables are measured. 

Feature selection, i.e. the selection of variables that are meaningful for the classification and elimination of those that have no discriminating (or, for certain techniques, no modelling power). This step is discussed further in Section 33.3. 

Derivation of a classification rule, using the training set. This is the subject of Section 33.2. 

---

Micheh-Tzanakou, E. (2000). Supervised and Unsupervised Pattern Recognition Feature Extraction and Computational Intelligence, CRC Press, Boca Raton, FL. 

---