Neural Network Algorithm for LDA/GSVD - Paredes, Rolysent;Sison, Ariel;Medina, Ruji
26,99 €
inkl. MwSt.
Versandkostenfrei*
Versandfertig in 6-10 Tagen
payback
13 °P sammeln
  • Broschiertes Buch

The capability of the classical Linear Discriminant Analysis based on Generalized Singular Value Decomposition (LDA/GSVD) deteriorates when dealing with unlabeled datasets because LDA requires predefined inputs and targets. In addition, the LDA/GSVD algorithm suffers from high computation cost due to its complex mathematical calculations and iterations. To address these problems, this study introduces Self-Organizing Map (SOM) as a new method in labeling datasets, and the development of an Artificial Neural Network-based algorithm to overcome the computational cost of LDA/GSVD. The results…mehr

Andere Kunden interessierten sich auch für
Produktbeschreibung
The capability of the classical Linear Discriminant Analysis based on Generalized Singular Value Decomposition (LDA/GSVD) deteriorates when dealing with unlabeled datasets because LDA requires predefined inputs and targets. In addition, the LDA/GSVD algorithm suffers from high computation cost due to its complex mathematical calculations and iterations. To address these problems, this study introduces Self-Organizing Map (SOM) as a new method in labeling datasets, and the development of an Artificial Neural Network-based algorithm to overcome the computational cost of LDA/GSVD. The results show that using SOM and ANN are effective in solving the problems of the traditional LDA/GSVD algorithm.
Autorenporträt
Rolysent Paredes est membre de la faculté de l'université de Misamis à Ozamiz City, aux Philippines. Il est instructeur certifié par l'Académie Cisco. Il a plusieurs publications à son actif et a présenté des travaux de recherche sur l'exploration de données, l'intelligence artificielle, l'apprentissage automatique et les réseaux informatiques lors de diverses conférences internationales.