Name
Abstract | ||
|---|---|---|
A novel algorithm named Adaptive Step Searching (ASS) is presented in the paper to solve the stochastic point location (SPL) problem. In the conventional method [1] for the SPL problem, the tradeoff between the convergence speed and accuracy is the main issue since the searching step of learning machine (LM) in the method is invariable during the entire searching. In that case, in ASS, LM adapts the step size to different situations during the searching. The convergence speed has been improved significantly with the same accuracy comparing to previous algorithms. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-39479-9_23 | ICIC (1) |
Keywords | Field | DocType |
adaptive step,step size,convergence speed,conventional method,spl problem,stochastic point location,stochastic point location problem,different situation,main issue,novel algorithm,previous algorithm,adaptive step searching | Learning machine,Convergence (routing),Learning automata,Point location,Computer science,Artificial intelligence,Machine learning | Conference |
Volume | ISSN | Citations |
7995 | 0302-9743 | 4 |
PageRank | References | Authors |
0.41 | 7 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tongtong Tao | 1 | 4 | 0.41 |
| Hao Ge | 2 | 9 | 4.76 |
| Guixian Cai | 3 | 4 | 0.41 |
| Shenghong Li | 4 | 357 | 47.31 |