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
8 Angebote vergleichen
| Preise | Feb. 17 | Juni 17 | März 19 |
|---|---|---|---|
| Schnitt | Fr. 48.80 (€ 49.90)¹ | Fr. 45.66 (€ 46.69)¹ | Fr. 48.17 (€ 49.26)¹ |
| Nachfrage |
1
Symbolbild
Algorithms for Prediction of Upper Body Power of Cross-Country Skiers
EN PB NWISBN: 9783330020290 bzw. 3330020296, in Englisch, LAP Lambert Academic Publishing, Taschenbuch, neu.
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.
2
Algorithms for Prediction of Upper Body Power of Cross-Country Skiers
DE NWISBN: 9783330020290 bzw. 3330020296, in Deutsch, neu.
Lieferung aus: Vereinigtes Königreich Grossbritannien und Nordirland, Lieferzeit: 11 Tage, zzgl. Versandkosten.
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.
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.
3
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
DE PB NWISBN: 9783330020290 bzw. 3330020296, in Deutsch, LAP Lambert Academic Publishing, Taschenbuch, neu.
Lieferung aus: Deutschland, Ücretsiz nakliye.
Algorithms for Prediction of Upper Body Power of Cross-Country Skiers: 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. Englisch, Taschenbuch.
Algorithms for Prediction of Upper Body Power of Cross-Country Skiers: 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. Englisch, Taschenbuch.
4
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
DE PB NWISBN: 9783330020290 bzw. 3330020296, in Deutsch, LAP Lambert Academic Publishing, Taschenbuch, neu.
Lieferung aus: Deutschland, Versandkostenfrei.
Algorithms for Prediction of Upper Body Power of Cross-Country Skiers: 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. Englisch, Taschenbuch.
Algorithms for Prediction of Upper Body Power of Cross-Country Skiers: 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. Englisch, Taschenbuch.
5
Algorithms for Prediction of Upper Body Power of Cross-Country Skiers
DE HC NWISBN: 9783330020290 bzw. 3330020296, in Deutsch, Lap Lambert Academic Publishing, gebundenes Buch, neu.
Lieferung aus: Deutschland, Versandkostenfrei innerhalb von Deutschland.
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 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. Lieferzeit 1-2 Werktage.
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 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. Lieferzeit 1-2 Werktage.
6
Algorithms for Prediction of Upper Body Power of Cross-Country Skiers
DE NWISBN: 3330020296 bzw. 9783330020290, in Deutsch, neu.
Algorithms for Prediction of Upper Body Power of Cross-Country Skiers ab 49.9 EURO Prediction of Upper Body Power of Cross-Country Skiers Using Machine Learning Methods Combined With Feature Selection.
7
Algorithms for Prediction of Upper Body Power (2017)
DE PB NWISBN: 9783330020290 bzw. 3330020296, in Deutsch, Taschenbuch, neu.
Lieferung aus: Deutschland, Next Day, Versandkostenfrei.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
8
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 (2016)
EN PB NWISBN: 9783330020290 bzw. 3330020296, in Englisch, 100 Seiten, LAP LAMBERT Academic Publishing, Taschenbuch, neu.
Lieferung aus: Vereinigtes Königreich Grossbritannien und Nordirland, Usually dispatched within 24 hours.
Von Händler/Antiquariat, Amazon.co.uk.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
Von Händler/Antiquariat, Amazon.co.uk.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
Lade…