Neural Network Algorithm for LDA/GSVD - 4 Angebote vergleichen

Lieferung aus: Deutschland, Versandkostenfrei.
Von Händler/Antiquariat, AHA-BUCH GmbH [51283250], Einbeck, Germany.
Neuware - 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. 80 pp. Englisch.

Lieferung aus: Deutschland, Versandkostenfrei.
Neural Network Algorithm for LDA/GSVD: 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. Englisch, Taschenbuch.

Lieferung aus: Deutschland, Versandkosten nach: Deutschland, Versandkostenfrei.
Von Händler/Antiquariat, Syndikat Buchdienst, [4235284].
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. 2019, Taschenbuch / Paperback, Neuware, H: 220mm, B: 150mm, 80, Internationaler Versand, Selbstabholung und Barzahlung, PayPal, offene Rechnung, Banküberweisung.

Lieferung aus: Deutschland, Gewöhnlich versandfertig in 24 Stunden, Versandkostenfrei. Tatsächliche Versandkosten können abweichen.
Von Händler/Antiquariat, averdo24.
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