Abstract:
:This paper presents the use of linear and nonlinear multivariable models as tools to support training process of race walkers. These models are calculated using data collected from race walkers' training events and they are used to predict the result over a 3 km race based on training loads. The material consists of 122 training plans for 21 athletes. In order to choose the best model leave-one-out cross-validation method is used. The main contribution of the paper is to propose the nonlinear modifications for linear models in order to achieve smaller prediction error. It is shown that the best model is a modified LASSO regression with quadratic terms in the nonlinear part. This model has the smallest prediction error and simplified structure by eliminating some of the predictors.
journal_name
Comput Intell Neuroscijournal_title
Computational intelligence and neuroscienceauthors
Wiktorowicz K,Przednowek K,Lassota L,Krzeszowski Tdoi
10.1155/2015/735060subject
Has Abstractpub_date
2015-01-01 00:00:00pages
735060eissn
1687-5265issn
1687-5273journal_volume
2015pub_type
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