A computational model of cognitive inductive reasoning that accounts for risk context effects is proposed. The model is based on a Support Vector Machine (SVM) that utilizes the kernel method. Kernel functions within the model are assumed to represent the functions of similarity computations based on distances between premise entities and conclusion entities in inductive reasoning arguments. Mu...

This paper studies a new framework for learning a predictor in the presence of multiple kernel functions where the learner selects or extracts several kernel functions from potentially complex families and finds an accurate predictor defined in terms of these functions. We present an algorithm, Voted Kernel Regularization, that provides the flexibility of using very complex kernel functions suc...

InKeV is a network virtualization platform based on eBPF, an in-kernel execution engine recently upstreamed into linux kernel. InKeV’s key contribution is that it enables in-kernel programmability and configuration of virtualized network functions, allowing to create a distributed virtual network across all edges hosting tenant workloads. Despite high performance demands of production environme...

The efficiency and performance of the Twin Support Vector Machines (TWSVM) are better than the traditional support vector machines when it deals with the problems. However, it also has the problem of selecting kernel functions. Generally, TWSVM selects the Gaussian radial basis kernel function. Although it has a strong learning ability, its generalization ability is relatively weak. In a certai...