Yücel eser (8 résultats)

- Couverture souple
Vendeur : moluna, Greven, Allemagnemoluna
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 45,45
EUR 48,99 expéditionExpédition depuis Allemagne vers Etats-UnisQuantité disponible : Plus de 20 disponibles
Etat : New.

Prediction of Vo2max With Submaximal and Questionnaire Variables: Using different regression methods
- Couverture souple
Vendeur : Books Puddle, Woodside, NY, Etats-UnisBooks Puddle
Contacter le vendeurVendeur avec une évaluation de 4 étoilesEtat: Neuf
EUR 98,44
EUR 3,61 expéditionExpédition nationale : Etats-UnisQuantité disponible : 4 disponibles
Etat : New.

- Couverture souple
Vendeur : preigu, Osnabrück, Allemagnepreigu
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 48,15
EUR 70,00 expéditionExpédition depuis Allemagne vers Etats-UnisQuantité disponible : 5 disponibles
Taschenbuch. Etat : Neu. Prediction of Vo2max With Submaximal and Questionnaire Variables | Using different regression methods | Eser Yücel (u. a.) | Taschenbuch | 96 S. | Englisch | 2015 | LAP LAMBERT Academic Publishing | EAN 9783659806155 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. …

- Couverture souple
- impression à la demande
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AllemagneBuchWeltWeit Ludwig Meier e.K.
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 54,90
EUR 23,00 expéditionExpédition depuis Allemagne vers Etats-UnisQuantité disponible : 2 disponibles
Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Maximal oxygen uptake (VO2max) refers to the maximum amount of oxygen that an individual can utilize during intense or maximal exercise. The purpose of this thesis is to develop accurate VO2max prediction models using submaximal and questionnaire variables. Regression methods such as Support Vector Machines (SVM), Multilayer Perceptron (MLP) and Multiple Linear Regression (MLR) have been used for developing VO2max prediction models. The performance of prediction models has been evaluated by calculating their multiple correlation coefficients (R's) and standard error of estimates (SEE's). The results show that the accuracy of VO2max prediction models based on submaximal and standard non-exercise variables could be significantly improved by including questionnaire variables in prediction models. The results of SVM models have been also compared with the ones obtained by MLP and MLR and it turned out that SVM-based VO2max prediction models perform better (i.e. yield lower SEE's and higher R's) than the prediction models developed by other regression methods. 96 pp. Englisch.…

- Couverture souple
- impression à la demande
Vendeur : AHA-BUCH GmbH, Einbeck, AllemagneAHA-BUCH GmbH
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 63,33
EUR 35,00 expéditionExpédition depuis Allemagne vers Etats-UnisQuantité disponible : 1 disponible
Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Maximal oxygen uptake (VO2max) refers to the maximum amount of oxygen that an individual can utilize during intense or maximal exercise. The purpose of this thesis is to develop accurate VO2max prediction models using submaximal and questionnaire variables. Regression methods such as Support Vector Machines (SVM), Multilayer Perceptron (MLP) and Multiple Linear Regression (MLR) have been used for developing VO2max prediction models. The performance of prediction models has been evaluated by calculating their multiple correlation coefficients (R's) and standard error of estimates (SEE's). The results show that the accuracy of VO2max prediction models based on submaximal and standard non-exercise variables could be significantly improved by including questionnaire variables in prediction models. The results of SVM models have been also compared with the ones obtained by MLP and MLR and it turned out that SVM-based VO2max prediction models perform better (i.e. yield lower SEE's and higher R's) than the prediction models developed by other regression methods.…

Prediction of Vo2max With Submaximal and Questionnaire Variables: Using different regression methods
- Couverture souple
- impression à la demande
Vendeur : Majestic Books, Hounslow, Royaume-UniMajestic Books
Contacter le vendeurVendeur avec une évaluation de 4 étoilesEtat: Neuf
EUR 100,23
EUR 7,78 expéditionExpédition depuis Royaume-Uni vers Etats-UnisQuantité disponible : 4 disponibles
Etat : New. Print on Demand.

Prediction of Vo2max With Submaximal and Questionnaire Variables: Using different regression methods
- Couverture souple
- impression à la demande
Vendeur : Biblios, frankfurt am main, HESSE, AllemagneBiblios
Contacter le vendeurVendeur avec une évaluation de 4 étoilesEtat: Neuf
EUR 99,27
EUR 9,95 expéditionExpédition depuis Allemagne vers Etats-UnisQuantité disponible : 4 disponibles
Etat : New. PRINT ON DEMAND.

- Couverture souple
- impression à la demande
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagnebuchversandmimpf2000
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 54,90
EUR 60,00 expéditionExpédition depuis Allemagne vers Etats-UnisQuantité disponible : 1 disponible
Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -Maximal oxygen uptake (VO2max) refers to the maximum amount of oxygen that an individual can utilize during intense or maximal exercise. The purpose of this thesis is to develop accurate VO2max prediction models using submaximal and questionnaire variables. Regression methods such as Support Vector Machines (SVM), Multilayer Perceptron (MLP) and Multiple Linear Regression (MLR) have been used for developing VO2max prediction models. The performance of prediction models has been evaluated by calculating their multiple correlation coefficients (R's) and standard error of estimates (SEE's). The results show that the accuracy of VO2max prediction models based on submaximal and standard non-exercise variables could be significantly improved by including questionnaire variables in prediction models. The results of SVM models have been also compared with the ones obtained by MLP and MLR and it turned out that SVM-based VO2max prediction models perform better (i.e. yield lower SEE's and higher R's) than the prediction models developed by other regression methods.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 96 pp. Englisch.…