Name
Abstract | ||
|---|---|---|
Since the beginning, some pattern recognition techniques have faced the problem of high computational burden for dataset learning. Among the most widely used techniques, we may highlight Support Vector Machines (SVM), which have obtained very promising results for data classification. However, this classifier requires an expensive training phase, which is dominated by a parameter optimization that aims to make SVM less prone to errors over the training set. In this paper, we model the problem of finding such parameters as a metaheuristic-based optimization task, which is performed through Harmony Search (HS) and some of its variants. The experimental results have showen the robustness of HS-based approaches for such task in comparison against with an exhaustive (grid) search, and also a Particle Swarm Optimization-based implementation. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/EUROCON.2013.6625103 | EUROCON |
Keywords | Field | DocType |
particle swarm optimisation,pattern recognition,support vector machines,harmony search,parameter optimization,particle swarm optimization,pattern recognition techniques,support vector machines,training optimization,Fault Detections,Harmony Search,Support Vector Machines | Particle swarm optimization,Derivative-free optimization,Computer science,Support vector machine,Robustness (computer science),Multi-swarm optimization,Artificial intelligence,Harmony search,Sequential minimal optimization,Machine learning,Metaheuristic | Conference |
ISBN | Citations | PageRank |
978-1-4673-2230-0 | 3 | 0.42 |
References | Authors | |
8 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luís A. M. Pereira | 1 | 129 | 8.87 |
| João P. Papa | 2 | 689 | 46.87 |
| André N. de Souza | 3 | 3 | 0.42 |