.png){kind=icon}
.png){kind=icon}
Return
Parallel online event calculus learning for complex event recognition
.png){kind=icon}
PRE
AI
DOI：10.1016/j.future.2018.11.033.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Logic-based event recognition systems infer occurrences of events in time using a set of event definitions in the form of first-order rules. The Event Calculus is a temporal logic that has been used as a basis in event recognition applications, providing, among others, direct connections to machine learning, via Inductive Logic Programming (ILP). OLED is a recently proposed ILP system that learns event definitions in the form of Event Calculus theories, in a single pass over a data stream. We present two strategies for parallel online learning with OLED. We evaluate our proposed approaches on three datasets from the domains of activity recognition and maritime surveillance and show that they can significantly reduce training times, while they are capable of achieving super-linear speed-ups under certain circumstances. (C) 2018 Elsevier B.V. All rights reserved.
Keywords:
Online learning
Parallel learning
Event calculus
Event recognition
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
F
IF:
6.1
Papers:
6.8K
Citations:
2.3W