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 - Brossura
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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.
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Informazioni sull?autore
Mustafa Mikail ÖZÇİLOĞLU 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.
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Algorithms for Prediction of Upper Body Power of Cross-Country Skiers
Editore:
LAP LAMBERT Academic Publishing Dez 2016, 2016
ISBN 10: 3330020296
ISBN 13: 9783330020290
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
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Taschenbuch. Condizione: 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. Codice articolo 9783330020290
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Algorithms for Prediction of Upper Body Power of Cross-Country Skiers
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LAP LAMBERT Academic Publishing, 2016
ISBN 10: 3330020296
ISBN 13: 9783330020290
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Algorithms for Prediction of Upper Body Power of Cross-Country Skiers
Editore:
LAP LAMBERT Academic Publishing Dez 2016, 2016
ISBN 10: 3330020296
ISBN 13: 9783330020290
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
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Taschenbuch. Condizione: 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, Überseering 33, 22297 Hamburg 100 pp. Englisch. Codice articolo 9783330020290
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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
Editore:
LAP LAMBERT Academic Publishing, 2016
ISBN 10: 3330020296
ISBN 13: 9783330020290
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Taschenbuch. Condizione: 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. Codice articolo 9783330020290
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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
Editore:
LAP LAMBERT Academic Publishing, 2016
ISBN 10: 3330020296
ISBN 13: 9783330020290
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Paperback. Condizione: Brand New. 100 pages. 8.66x5.91x0.23 inches. In Stock. Codice articolo __3330020296
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