Stochastic organization of output codes in multiclass learning problems.
The best-known decomposition schemes of multiclass learning problems are one per class coding (OPC) and error-correcting output coding (ECOC). Both methods perform a prior decomposition, that is, before training of the classifier takes place. The impact of output codes on the inferred decision rules can be experienced only after learning. Therefore, we present a novel algorithm for the code design of multiclass learning problems. This algorithm applies a maximum-likelihood objective function in conjunction with the expectation-maximization (EM) algorithm. Minimizing the augmented objective function yields the optimal decomposition of the multiclass learning problem in two-class problems. Experimental results show the potential gain of the optimized output codes over OPC or ECOC methods.
Utschick
W
W
Institute for Network Theory and Signal Processing, Munich University of Technology, D-80290 Munich, Germany.
Weichselberger
W
W
D000465:Algorithms; D007858:Learning; D016013:Likelihood Functions; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology; D016571:Neural Networks, Computer; D011336:Probability; D013269:Stochastic Processes