This allows for representing pattern structures, taking into account more complex relationships between attributes than is possible in the case of flat, numerical feature vectors of fixed dimensionality that are used in statistical classification.
If normal and unhealthy waveforms can be described as formal grammars, ECG signals can be classified as healthy or unhealthy by first describing them in terms of the basic line segments, and then trying to parse the descriptions according to the grammars.
This helps divide the recognition task into easier subtasks of first identifying sub-patterns, and then the actual patterns.
Furthermore, structural methods are strong when applied to finding a "correspondence mapping" between two images of an object.
Under natural conditions, corresponding features will be in different positions and/or may be occluded in the two images, due to camera attitude and perspective, as in face recognition.
Structural and syntactic pattern recognition, Chen, Pau & Wang (Eds.)