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Synopsis
The purpose of this work is to develop VO2max prediction models by using non-exercise, submaximal and hybrid variables by using Support Vector Machines (SVM), Multi-layer Feed-forward Artificial Neural Networks (MFANN) and Multiple Linear Regression (MLR) on different data sets. Using 10-fold cross validation on four different data sets, 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 SVM-based VO2max prediction models perform better (i.e. yield lower SEE’s and higher R’s) than the prediction models developed by MFANN and MLR. We also propose a new approach based on the elimination of irrelevant samples during the training phase for improving the performance of SVM and MFANN models for prediction of VO2max. The performance of the proposed approach has been compared with the two widely used outlier detection algorithms. The results show that the improved SVM-based and MFANN-based VO2max prediction models yield noticeable decrements in error rates compared to that of regular and outlier-based SVM and MFANN VO2max prediction models.
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About the Author
Dr. Mustafa Acikkar graduated from the Department of Electrical and Electronics Engineering of ÇUkurova University. He completed his MSc and PhD studies at Çukurova University as well. He currently has 2 journal papers and 10 conference papers.
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- PublisherLAP LAMBERT Academic Publishing
- Publication date2015
- ISBN 10 3659806145
- ISBN 13 9783659806148
- BindingPaperback
- LanguageEnglish
- Number of pages184
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Developing VO2max Prediction Models Using Machine Learning Methods
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LAP LAMBERT Academic Publishing Nov 2015, 2015
ISBN 10: 3659806145
ISBN 13: 9783659806148
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The purpose of this work is to develop VO2max prediction models by using non-exercise, submaximal and hybrid variables by using Support Vector Machines (SVM), Multi-layer Feed-forward Artificial Neural Networks (MFANN) and Multiple Linear Regression (MLR) on different data sets. Using 10-fold cross validation on four different data sets, 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 SVM-based VO2max prediction models perform better (i.e. yield lower SEE's and higher R's) than the prediction models developed by MFANN and MLR. We also propose a new approach based on the elimination of irrelevant samples during the training phase for improving the performance of SVM and MFANN models for prediction of VO2max. The performance of the proposed approach has been compared with the two widely used outlier detection algorithms. The results show that the improved SVM-based and MFANN-based VO2max prediction models yield noticeable decrements in error rates compared to that of regular and outlier-based SVM and MFANN VO2max prediction models. 184 pp. Englisch. Seller Inventory # 9783659806148
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Developing VO2max Prediction Models Using Machine Learning Methods
Published by
LAP LAMBERT Academic Publishing, 2015
ISBN 10: 3659806145
ISBN 13: 9783659806148
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Taschenbuch
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The purpose of this work is to develop VO2max prediction models by using non-exercise, submaximal and hybrid variables by using Support Vector Machines (SVM), Multi-layer Feed-forward Artificial Neural Networks (MFANN) and Multiple Linear Regression (MLR) on different data sets. Using 10-fold cross validation on four different data sets, 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 SVM-based VO2max prediction models perform better (i.e. yield lower SEE's and higher R's) than the prediction models developed by MFANN and MLR. We also propose a new approach based on the elimination of irrelevant samples during the training phase for improving the performance of SVM and MFANN models for prediction of VO2max. The performance of the proposed approach has been compared with the two widely used outlier detection algorithms. The results show that the improved SVM-based and MFANN-based VO2max prediction models yield noticeable decrements in error rates compared to that of regular and outlier-based SVM and MFANN VO2max prediction models. Seller Inventory # 9783659806148
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Developing VO2max Prediction Models Using Machine Learning Methods
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ISBN 13: 9783659806148
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Developing VO2max Prediction Models Using Machine Learning Methods
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ISBN 10: 3659806145
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