Algorithms for Prediction of Upper Body Power of Cross-Country Skiers: Prediction of Upper Body Power of Cross-Country Skiers Using Machine Learning Methods Combined With Feature Selection - Couverture souple

�z�iloğlu, Mustafa Mikail; Akay, Mehmet Fatih

9783330020290: Algorithms for Prediction of Upper Body Power of Cross-Country Skiers: Prediction of Upper Body Power of Cross-Country Skiers Using Machine Learning Methods Combined With Feature Selection

Couverture souple

ISBN 10 : 3330020296 ISBN 13 : 9783330020290

Editeur : LAP LAMBERT Academic Publishing, 2016

Afficher les exemplaires de cette �dition comportant l?ISBN

0 D'occasion

5 Neuf

De EUR 41,71

Upper body power (UBP) is one of the most important factors affecting the performance of cross-country skiers during races. The purpose of this study is to develop new prediction models for predicting the 10-second UBP (UBP10) and 60-second UBP (UBP60) of cross-country skiers by using General Regression Neural Networks (GRNN), Radial-Basis Function Network (RBF), Multilayer Perceptron (MLP), Support Vector Machine (SVM), Single Decision Tree (SDT) and Tree Boost (TB) along with the Relief-F feature selection algorithm, minimum redundancy maximum relevance (mRMR) feature selection algorithm and the Correlation-based Feature Subset Selection (CFS). Several models have been developed to predict UBP10 and UBP60 of cross-country skiers using two datasets. 10-fold cross validation has been performed for model testing. The efficiency of the prediction models has been calculated with their multiple correlation coefficients (R’s), standard error of estimates (SEE’s) and mean absolute percentage errors (MAPE’s). The results emphasize that GRNN-based prediction models show higher performance than the other regression methods.

Les informations fournies dans la section � Synopsis � peuvent faire r�f�rence � une autre �dition de ce titre.

� propos de l?auteur

Mustafa Mikail �Z�İLOĞLU was born in Kilis, Turkey, in 1985. He received his BSc, MSc and PhD degrees from the Department of Computer Engineering of Mersin University, the Department of Computer Engineering of TOBB Economy and Technology University and the Department of Electrical and Electronics Engineering of Cukurova University, respectively.

Les informations fournies dans la section � A propos du livre � peuvent faire r�f�rence � une autre �dition de ce titre.

�diteurLAP LAMBERT Academic Publishing
Date d'�dition2016
ISBN 10 3330020296
ISBN 13 9783330020290
ReliureBroch�
Langueanglais
Nombre de pages100
Coordonn�es du fabricantnon disponible

Acheter neuf

Afficher cet article

EUR 41,71

Autre devise

EUR 9,70 exp�dition depuis Allemagne vers France

Destinations, frais et d�lais

Ajouter au panier

R�sultats de recherche pour Algorithms for Prediction of Upper Body Power of Cross-Count...

Image fournie par le vendeur

Algorithms for Prediction of Upper Body Power of Cross-Country Skiers

Mustafa Mikail �z�iloglu|Mehmet Fatih Akay

Edit� par LAP LAMBERT Academic Publishing, 2016

ISBN 10 : 3330020296 ISBN 13 : 9783330020290

Neuf Couverture souple

Vendeur : moluna, Greven, Allemagne

�valuation du vendeur 5 sur 5 �toiles

Etat : New. N� de r�f. du vendeur 158590259

Contacter le vendeur

Acheter neuf

EUR 41,71

Autre devise

Frais de port : EUR 9,70

De Allemagne vers France

Destinations, frais et d�lais

Quantit� disponible : Plus de 20 disponibles

Ajouter au panier

Image fournie par le vendeur

Algorithms for Prediction of Upper Body Power of Cross-Country Skiers : Prediction of Upper Body Power of Cross-Country Skiers Using Machine Learning Methods Combined With Feature Selection

Mustafa Mikail �z�ilo�lu

Edit� par LAP LAMBERT Academic Publishing, 2016

ISBN 10 : 3330020296 ISBN 13 : 9783330020290

Neuf Taschenbuch

impression � la demande

Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne

�valuation du vendeur 5 sur 5 �toiles

Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Upper body power (UBP) is one of the most important factors affecting the performance of cross-country skiers during races. The purpose of this study is to develop new prediction models for predicting the 10-second UBP (UBP10) and 60-second UBP (UBP60) of cross-country skiers by using General Regression Neural Networks (GRNN), Radial-Basis Function Network (RBF), Multilayer Perceptron (MLP), Support Vector Machine (SVM), Single Decision Tree (SDT) and Tree Boost (TB) along with the Relief-F feature selection algorithm, minimum redundancy maximum relevance (mRMR) feature selection algorithm and the Correlation-based Feature Subset Selection (CFS). Several models have been developed to predict UBP10 and UBP60 of cross-country skiers using two datasets. 10-fold cross validation has been performed for model testing. The efficiency of the prediction models has been calculated with their multiple correlation coefficients (R's), standard error of estimates (SEE's) and mean absolute percentage errors (MAPE's). The results emphasize that GRNN-based prediction models show higher performance than the other regression methods. N� de r�f. du vendeur 9783330020290

Contacter le vendeur

Acheter neuf

EUR 49,90

Autre devise

Frais de port : EUR 10,99

De Allemagne vers France

Destinations, frais et d�lais

Quantit� disponible : 1 disponible(s)

Ajouter au panier

Image fournie par le vendeur

Algorithms for Prediction of Upper Body Power of Cross-Country Skiers

Mustafa Mikail �z�ilo�lu

Edit� par LAP LAMBERT Academic Publishing Dez 2016, 2016

ISBN 10 : 3330020296 ISBN 13 : 9783330020290

Neuf Taschenbuch

impression � la demande

Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne

�valuation du vendeur 5 sur 5 �toiles

Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Upper body power (UBP) is one of the most important factors affecting the performance of cross-country skiers during races. The purpose of this study is to develop new prediction models for predicting the 10-second UBP (UBP10) and 60-second UBP (UBP60) of cross-country skiers by using General Regression Neural Networks (GRNN), Radial-Basis Function Network (RBF), Multilayer Perceptron (MLP), Support Vector Machine (SVM), Single Decision Tree (SDT) and Tree Boost (TB) along with the Relief-F feature selection algorithm, minimum redundancy maximum relevance (mRMR) feature selection algorithm and the Correlation-based Feature Subset Selection (CFS). Several models have been developed to predict UBP10 and UBP60 of cross-country skiers using two datasets. 10-fold cross validation has been performed for model testing. The efficiency of the prediction models has been calculated with their multiple correlation coefficients (R's), standard error of estimates (SEE's) and mean absolute percentage errors (MAPE's). The results emphasize that GRNN-based prediction models show higher performance than the other regression methods. 100 pp. Englisch. N� de r�f. du vendeur 9783330020290

Contacter le vendeur

Acheter neuf

EUR 49,90

Autre devise

Frais de port : EUR 11

De Allemagne vers France

Destinations, frais et d�lais

Quantit� disponible : 2 disponible(s)

Ajouter au panier

Image fournie par le vendeur

Algorithms for Prediction of Upper Body Power of Cross-Country Skiers

Mustafa Mikail �z�ilo�lu

Edit� par LAP LAMBERT Academic Publishing Dez 2016, 2016

ISBN 10 : 3330020296 ISBN 13 : 9783330020290

Neuf Taschenbuch

Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne

�valuation du vendeur 5 sur 5 �toiles

Taschenbuch. Etat : Neu. Neuware -Upper body power (UBP) is one of the most important factors affecting the performance of cross-country skiers during races. The purpose of this study is to develop new prediction models for predicting the 10-second UBP (UBP10) and 60-second UBP (UBP60) of cross-country skiers by using General Regression Neural Networks (GRNN), Radial-Basis Function Network (RBF), Multilayer Perceptron (MLP), Support Vector Machine (SVM), Single Decision Tree (SDT) and Tree Boost (TB) along with the Relief-F feature selection algorithm, minimum redundancy maximum relevance (mRMR) feature selection algorithm and the Correlation-based Feature Subset Selection (CFS). Several models have been developed to predict UBP10 and UBP60 of cross-country skiers using two datasets. 10-fold cross validation has been performed for model testing. The efficiency of the prediction models has been calculated with their multiple correlation coefficients (R�s), standard error of estimates (SEE�s) and mean absolute percentage errors (MAPE�s). The results emphasize that GRNN-based prediction models show higher performance than the other regression methods.Books on Demand GmbH, �berseering 33, 22297 Hamburg 100 pp. Englisch. N� de r�f. du vendeur 9783330020290

Contacter le vendeur