Representing and reasoning with an agent's preferences is important in many applications of constraints formalisms. Such preferences are often only partially ordered. One clas...
The neural network (NN) models well trained and validated by the same data may exhibit noticeably different predictabilities in applications. This is mainly due to the fact that t...
A hybrid learning neuro-fuzzy system with asymmetric fuzzy sets (HLNFS-A) is proposed in this paper. The learning methods of random optimization (RO) and least square estimation (...
Vector, emerging (homogenous and heterogeneous) multi-core and a number of accelerator processing devices potentially offer an order of magnitude speedup for scientific application...
Real world data mining applications must address the issue of learning from imbalanced data sets. The problem occurs when the number of instances in one class greatly outnumbers t...