Margit Kinder and Wilfried Brauer,
Classification of Trajectories - Extracting Invariants
With a Neural Network,
Neural Networks,
6, pp. 1011-1017, 1993.
Margit Kinder and Wilfried Brauer,
Classification of Trajectories - Extracting Invariants
With a Neural Network,
Neural Networks,
6, pp. 1011-1017, 1993.A neural classifier of planar trajectories is presented. There already exist a large variety of classifiers that are specialized in particular invariants contained in a trajectory classification task such as position-invariance, rotation-invariance and size-invariance. That is, there exist classifiers specialized in recognizing trajectories, e.g., independently of their position. The neural classifier presented in this paper is not restricted to certain invariants in a task: The neural network itself extracts the invariants contained in a classification task by assessing only the trajectories. The trajectories need to be given as a set of points. No additional information must be available for training, which saves the designer from determining the needed invariants by himself. Besides its applicability to real-world problems, such a more general classifier is also cognitively plausible: In assessing trajectories for classification, human beings are able to find class specific features no matter what kinds of invariants they are confronted with. Invariants are easily handled by ignoring unspecific features.