Name
Abstract | ||
|---|---|---|
This paper introduces a new approach to enhance learning in adjustment processes by using a support vector machine (SVM) algorithm as discriminant function jointly with an action generator module. The method trains a SVM with state-action patterns and uses trained SVM to select an appropriate action given a certain state in order to reach the target state. The system incorporates a simulated annealing technique to increase the exploration capacity and improve the ability to avoid local minima. The methodology has been tested in an example with artificial data. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.3233/978-1-60750-061-2-119 | CCIA |
Keywords | Field | DocType |
enhance learning,target state,adjustment process,artificial data,certain state,exploration capacity,appropriate action,simulated annealing,action generator module,new approach,adjustment processes,local minimum,discriminant function | Simulated annealing,Online machine learning,Active learning (machine learning),Pattern recognition,Computer science,Support vector machine,Maxima and minima,Adaptive simulated annealing,Artificial intelligence,Discriminant function analysis | Conference |
Volume | ISSN | Citations |
202 | 0922-6389 | 0 |
PageRank | References | Authors |
0.34 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Albert Samà | 1 | 211 | 18.28 |
| Francisco Ruiz | 2 | 301 | 29.12 |
| Núria Agell | 3 | 199 | 30.62 |
| Cecilio Angulo | 4 | 434 | 57.48 |