|Introduction: Statistical Pattern Recognition Toolbox||Home|
This toolbox implements a selection of statistical pattern recognition methods described in the monograph M.I. Schlesinger, V. Hlavac: Ten lectures on the statistical and structural pattern recognition, Kluwer Academic Publishers, 2002 , rather it implements the first part of the monograph which deals with feature based statistical pattern recognition methods. The toolbox is still being developed and new implemented methods (see implemeted methods) go beyond the contents of the monograph.
The toolbox should help to understand relevant algorithms from the monograph and to demonstrate their functionality. The visualization of the process leading to the solution and experimentation feasibility is stressed for this reason. The demonstrator environment is provided that allows the user to choose different algorithms, compare their behavior, provides tools to control the algorithm run interactively and creates synthetic input data or uses real ones. The implemented method can be used for real applications as well.
The toolbox is build on top of the Matlab, version 5.3. and above. The reason for this choice is that Matlab provides many useful tools for data visualization, calculation with matrices, and the user interface independent on the operating system.
The toolbox is documented in a user's guide and on-line reference manual. Most implemented methods are demonstrated by a working example (see examples).
The toolbox can be used provided the license conditions are met.