A multivalent logic approach to risk estimation of learning machines
B. Novak · 2004
A multivalent logic approach to estimating the risk of error on test samples of learning machine is developed and compared to the bivalent approach based on VC dimension, cover and entropy numbers of sets. The multivalent approach leads to more simple expressions for predicting bounds on the risk estimation which are computable in a short time and use a reasonable amount of computer memory. The results of testing reveal that the multivalent logic algorithm outperforms support vector machines.