6533b835fe1ef96bd129f713
RESEARCH PRODUCT
Improved SOM Learning using Simulated Annealing
Antonino FiannacaAlfonso UrsoGiuseppe Di FattaSalvatore GaglioRiccardo Rizzosubject
SpeedupMatching (graph theory)Wake-sleep algorithmComputer sciencebusiness.industryPattern recognitioncomputer.software_genreAdaptive simulated annealingGeneralization errorComputingMethodologies_PATTERNRECOGNITIONSimulated annealingSOM simulated Annealing TrainingData miningArtificial intelligencebusinesscomputerdescription
Self-Organizing Map (SOM) algorithm has been extensively used for analysis and classification problems. For this kind of problems, datasets become more and more large and it is necessary to speed up the SOM learning. In this paper we present an application of the Simulated Annealing (SA) procedure to the SOM learning algorithm. The goal of the algorithm is to obtain fast learning and better performance in terms of matching of input data and regularity of the obtained map. An advantage of the proposed technique is that it preserves the simplicity of the basic algorithm. Several tests, carried out on different large datasets, demonstrate the effectiveness of the proposed algorithm in comparison with the original SOM and with some of its modification introduced to speed-up the learning.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2007-01-01 |