In this paper, we model multi-agent events in terms of a temporally varying sequence of sub-events, and propose a novel approach for learning, detecting and representing events in...
Background: Machine-learning tools have gained considerable attention during the last few years for analyzing biological networks for protein function prediction. Kernel methods a...
Echo State Networks (ESNs) have been shown to be effective for a number of tasks, including motor control, dynamic time series prediction, and memorizing musical sequences. Howeve...
Matthew H. Tong, Adam D. Bickett, Eric M. Christia...
Learning structured representations has emerged as an important problem in many domains, including document and Web data mining, bioinformatics, and image analysis. One approach t...
Anon Plangprasopchok, Kristina Lerman, Lise Getoor
—Inspired by the biological entities’ ability to achieve reciprocity in the course of evolution, this paper considers a conjecture-based distributed learning approach that enab...