We present prototype-based classification schemes, e. g. learning vector quantization, with cost-function-based and geometrically motivated reject options. We evaluate the reject schemes in experiments on artificial and benchmark data sets. We demonstrate that reject options improve the accuracy of the models in most cases, and that the performance of the proposed schemes is comparable to the o...

Abstract One approach to military leadership is the philosophy of mission command. Mission command supported by several principles and all them are essential, without those it cannot be fully applied. These mutual trust between participants, understanding, clearly articulated intent superior, decentralized execution timely-made decisions. As Artificial Intelligence progresssing gaining ground w...

$EVWUDFW A reject rule devised for a neural classifier based on the Learning Vector Quantization (LVQ) paradigm is presented. The reject option is carried out adaptively to the specific application domain. It is assumed that a performance function P is defined which, taking into account the requirements of a given application expressed in terms of classification, misclassification and reject co...

In pattern recognition systems, Chow’s rule is commonly used to reach a trade-off between error and reject probabilities. In this paper, we investigate the effects of estimate errors affecting the a posteriori probabilities on the optimality of Chow’s rule. We show that the optimal error-reject tradeoff is not provided by Chow’s rule if the a posteriori probabilities are affected by errors. The...