Yücel eser (8 resultados)
Prediction of Vo2max With Submaximal and Questionnaire Variables: Using different regression methods
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Librería: Books Puddle, New York, NY, Estados Unidos de AmericaBooks Puddle
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EUR 90,73
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Librería: moluna, Greven, Alemaniamoluna
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EUR 45,45
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Librería: preigu, Osnabrück, Alemaniapreigu
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EUR 47,90
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Taschenbuch. Condición: 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.
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.
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EUR 54,90
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Taschenbuch. Condición: 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.
Prediction of Vo2max With Submaximal and Questionnaire Variables: Using different regression methods
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Librería: Majestic Books, Hounslow, Reino UnidoMajestic Books
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EUR 92,22
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Prediction of Vo2max With Submaximal and Questionnaire Variables: Using different regression methods
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Librería: Biblios, frankfurt am main, HESSE, AlemaniaBiblios
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EUR 91,73
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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemaniabuchversandmimpf2000
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EUR 54,90
Envío por EUR 60,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Taschenbuch. Condición: 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 que…stionnaire 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.
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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
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EUR 55,56
Envío por EUR 60,81Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Taschenbuch. Condición: 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 ques…tionnaire 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.
