Supervised Pattern Recognition

Pattern recognition is the scientific discipline that focuses on the classification of data, objects or, in general terms, patterns into categories or classes. To achieve this goal, the methodology uses the extraction of information from the data observation, learn to recognize the different patterns contained within the data and make a decision based on the category of the patterns. This involves supervised classification methods, which are based on external knowledge of the area within the sample to be studied, and therefore, requires some a priori information before the chosen classification algorithm can be applied. The supervised methods are implemented using two main paradigms, statistical algorithms, and neural algorithms. The statistical approach uses parameters that are derived from sampled data in the form of training classes. The neural approach does not rely on statistical information derived from the sample data but is trained directly on the sample data