Titlebook: Advances in Self-Organizing Maps; 7th International Wo José C. Príncipe,Risto Miikkulainen Conference proceedings 2009 Springer-Verlag Berl

analogous

Fault Prediction in Aircraft Engines Using Self-Organizing Maps,s. The goal is to ensure a proper operation of the engines, in all conditions, with a zero probability of failure, while taking into account aging. The fact that the same engine is sometimes used on several aircrafts has to be taken into account too..The maintenance can be improved if an efficient p

技術(shù)

Incremental Figure-Ground Segmentation Using Localized Adaptive Metrics in LVQ,ss. In this paper we present an incremental learning scheme in the context of figure-ground segmentation. In presence of local adaptive metrics and supervised noisy information we use a parallel evaluation scheme combined with a local utility function to organize a learning vector quantization (LVQ)

阻撓

Application of Supervised Pareto Learning Self Organizing Maps and Its Incremental Learning,tors and applied SP-SOM to the biometric authentication system which uses multiple behavior characteristics as feature vectors. In this paper, we examine performance of SP-SOM for the generic classification problem using iris data set. Furthermore, we propose the incremental learning algorithm for S

Demonstrate

Entropion

甜得發(fā)膩

dissolution

Cartograms, Self-Organizing Maps, and Magnification Control,rts with a brief explanation of what a cartogram is, how it can be used, and what sort of metrics can be used to assess its quality. The methodology for creating a cartogram with a SOM is then presented together with an explanation of how the magnification effect can be compensated in this case by p

arousal

ViSOM for Dimensionality Reduction in Face Recognition,OM) and growing ViSOM (gViSOM) are two recently proposed variants for a more faithful, metric-based and direct data representation. They learn local quantitative distances of data by regularizing the inter-neuron contraction force while capturing the topology and minimizing the quantization error. I

Excise

寒冷