Name
Abstract | ||
|---|---|---|
Speculative execution of information gathering plans can dramatically reduce the effect of source I/O latencies on overall performance. However, the utility of speculation is closely tied to how accurately data values are predicted at runtime. Caching ... |
Year | Venue | Keywords |
|---|---|---|
2003 | IJCAI | information gathering plan,o latency,large-margin classifier,speculative execution,data value,overall performance,approximate policy iteration,support vector machine,state space |
Field | DocType | Citations |
Inverted pendulum,Mathematical optimization,Margin (machine learning),Random subspace method,Computer science,Support vector machine,Fixed-point iteration,Artificial intelligence,Classifier (linguistics),Margin classifier,State space,Machine learning | Conference | 0 |
PageRank | References | Authors |
0.34 | 6 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michail G. Lagoudakis | 1 | 1164 | 79.51 |
| Ronald Parr | 2 | 2428 | 186.85 |