Yücel eser (8 risultati)

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  • Lingua: Inglese

    Editore: LAP LAMBERT Academic Publishing, 2015

    3659806153 / 9783659806155

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    Da: moluna, Greven, Germaniamoluna

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    Condizione: Nuovo

    EUR 45,45

    EUR 48,99 spedizione 
    Spedito da Germania a U.S.A.

    Quantità: Più di 20 disponibili

    Condizione: New.

  • Lingua: Inglese

    Editore: LAP LAMBERT Academic Publishing, 2015

    3659806153 / 9783659806155

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    Da: Books Puddle, Woodside, NY, U.S.A.Books Puddle

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    Condizione: Nuovo

    EUR 94,27

    EUR 3,51 spedizione 
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    Quantità: 4 disponibili

    Condizione: New.

  • Lingua: Inglese

    Editore: LAP LAMBERT Academic Publishing, 2015

    3659806153 / 9783659806155

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    Da: preigu, Osnabrück, Germaniapreigu

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    Condizione: Nuovo

    EUR 47,95

    EUR 70,00 spedizione 
    Spedito da Germania a U.S.A.

    Quantità: 5 disponibili

    Taschenbuch. Condizione: 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. …

  • Lingua: Inglese

    Editore: LAP LAMBERT Academic Publishing Nov 2015, 2015

    3659806153 / 9783659806155

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    • Print on Demand

    Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.

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    Condizione: Nuovo

    EUR 54,90

    EUR 23,00 spedizione 
    Spedito da Germania a U.S.A.

    Quantità: 2 disponibili

    Taschenbuch. Condizione: 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.…

  • Lingua: Inglese

    Editore: LAP LAMBERT Academic Publishing, 2015

    3659806153 / 9783659806155

    • Brossura
    • Print on Demand

    Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH

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    Condizione: Nuovo

    EUR 57,33

    EUR 35,00 spedizione 
    Spedito da Germania a U.S.A.

    Quantità: 1 disponibili

    Taschenbuch. Condizione: 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.…

  • Lingua: Inglese

    Editore: LAP LAMBERT Academic Publishing, 2015

    3659806153 / 9783659806155

    • Brossura
    • Print on Demand

    Da: Majestic Books, Hounslow, Regno UnitoMajestic Books

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    Condizione: Nuovo

    EUR 94,10

    EUR 7,56 spedizione 
    Spedito da Regno Unito a U.S.A.

    Quantità: 4 disponibili

    Condizione: New. Print on Demand.

  • Lingua: Inglese

    Editore: LAP LAMBERT Academic Publishing, 2015

    3659806153 / 9783659806155

    • Brossura
    • Print on Demand

    Da: Biblios, frankfurt am main, HESSE, GermaniaBiblios

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    Condizione: Nuovo

    EUR 94,61

    EUR 9,95 spedizione 
    Spedito da Germania a U.S.A.

    Quantità: 4 disponibili

    Condizione: New. PRINT ON DEMAND.

  • Lingua: Inglese

    Editore: LAP LAMBERT Academic Publishing Nov 2015, 2015

    3659806153 / 9783659806155

    • Brossura
    • Print on Demand

    Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000

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    Condizione: Nuovo

    EUR 54,90

    EUR 60,00 spedizione 
    Spedito da Germania a U.S.A.

    Quantità: 1 disponibili

    Taschenbuch. Condizione: 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.…