Copertina di Developing VO2max Prediction Models Using Machine Learning Methods
Titolo del libro:

Developing VO2max Prediction Models Using Machine Learning Methods

LAP LAMBERT Academic Publishing (24.11.2015 )

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ISBN-13:

978-3-659-80614-8

ISBN-10:
3659806145
EAN:
9783659806148
Lingua del libro:
Inglese
Risvolto di copertina:
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.
Casa editrice:
LAP LAMBERT Academic Publishing
Sito Web:
https://www.lap-publishing.com/
Da (autore):
Mustafa Açıkkar, Mehmet Fatih Akay
Numero di pagine:
184
Pubblicato il:
24.11.2015
Giacenza di magazzino:
Disponibile
categoria:
Informatica, Elaborazione elettronica dati
Prezzo:
71,90 €
Parole chiave:
Machine Learning, Maximum oxygen uptake, prediction

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