Taxonomy of Pattern Classification Algorithms
- 201 Downloads
Two or three hundred different pattern classification algorithms have been suggested in literature during the last 50 years. The main objective of this chapter is to review a selection of known statistical algorithms that can be obtained or improved by training ANN-based classification systems. The first selection contains seven statistical algorithms that can be obtained while training linear and non-linear single layer perceptrons and the second selection contains algorithms that can be approached in ANN training after deriving new non-linear features from the original ones. Particular attention is given to methods which can be used to structure the covariance matrices and describe them by a small number of parameters. This approach is not very popular in statistical pattern recognition, however, together with utilisation of neural networks, it becomes a powerful tool to solve problems in small training-set situations.
KeywordsDecision Boundary Classification Rule Pattern Class Training Vector Decision Tree Classifier
Unable to display preview. Download preview PDF.