Multiple classifier system based on attractor dynamics

A new method is proposed for combining outputs of several classifiers. The method is based on the theory of dynamical systems. In our formulation each classifier output represents a forcelet in a phase space of the dynamical system. Depending on interactions (superposition) of forcelets the system can perform in two different regimes: non linear averaging among several classifiers outputs and selection among them. We show that the attractor dynamics method outperforms both the winner takes all algorithm and the single best classifier.