Title :
Data compression methods with using neurolike GTM structures
Author :
Polishchuk, Uliana ; Tkachenko, Pavlo ; Tkachenko, Roman ; Yurchak, Irina
Author_Institution :
Lviv Polytech. Nat. Univ., Lviv
Abstract :
Applying neural networks for data compression provides compression with very good generalization characteristics with management losses or even without any information losses. In the basis of applying neural networks for data compression - is its training without supervisor (self-training).
Keywords :
data compression; neural nets; unsupervised learning; data compression method; information loss; management loss; neural network; neurolike GTM structure; self-training method; Data compression; Electronic mail; Entropy; Management training; Multi-layer neural network; Neural networks; Neurons; Principal component analysis; Quantization; Signal generators; Auto-associative neural network; GTM neurolike structures; Geometrical Transformation Machine; Reducing of the data dimension; Reducing of the data multiplicity; Training without supervisor (self-training);
Conference_Titel :
Perspective Technologies and Methods in MEMS Design, 2009. MEMSTECH 2009. 2009 5th International Conference on
Conference_Location :
Zakarpattya
Print_ISBN :
978-966-2191-06-6
