Scheduling Algorithms for Multiprogramming in a Hard-Real-Time Environment
Journal of the ACM (JACM)
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Real-Time Systems
Hard Real-Time Computing Systems: Predictable Scheduling Algorithms and Applications
Hard Real-Time Computing Systems: Predictable Scheduling Algorithms and Applications
Digital Control of Dynamic Systems
Digital Control of Dynamic Systems
Information Retrieval: Algorithms and Heuristics
Information Retrieval: Algorithms and Heuristics
Fast Text Classification: A Training-Corpus Pruning Based Approach
DASFAA '03 Proceedings of the Eighth International Conference on Database Systems for Advanced Applications
A scalability analysis of classifiers in text categorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Real Time Scheduling Theory: A Historical Perspective
Real-Time Systems
Rate monotonic vs. EDF: judgment day
Real-Time Systems
Efficient semantic kernel-based text classification using matching pursuit KFDA
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
| Hi-index | 0.00 |
This paper proposes a framework for soft real-time text classification system, which use control theory as a scientific underpinning, rather than ad hoc solutions. In order to provide real-time guarantee, two control loops are adopted. The feed forward control loop estimates the suitable number of classifiers according to the current workload, while the feedback control loop provides fine-grained control to the number of classifiers that perform imprecise computation. The soft real-time classification system can accommodate to the change of workload and transitional overload. The theory analysis and experiments result further prove its effectiveness: the variation range of the average response time is kept within ± 3% of the desired value; the computational resource is dynamically reallocated and reclaimed.