Kernel machines have been shown as the state-of-the-art learning techniques for classification. In this paper, we propose a novel general framework of learning the Unified Kernel ...
After a discussion on definability of invariant subdivision rules we discuss rules for sequential data living in Riemannian manifolds and in symmetric spaces, having in mind the s...
Johannes Wallner, Esfandiar Nava Yazdani, Andreas ...
The problem of graph classification has attracted great interest in the last decade. Current research on graph classification assumes the existence of large amounts of labeled tra...
This paper describes results concerning the robustness and generalization capabilities of kernel methods in detecting coordinated distributed multiple attacks (CDMA) using network...
Srinivas Mukkamala, Krishna Yendrapalli, Ram B. Ba...
Modern GPUs offer much computing power at a very modest cost. Even though CUDA and other related recent developments are accelerating the use of GPUs for general purpose applicati...