Authors:
Darko Katović
1
and
Miljenko Cvjetko
2
Affiliations:
1
Faculty of Kinesiology, University of Zagreb and Croatia
;
2
Microsoft (Xamarin Inc.), Software Engineer, Zagreb and Croatia
Keyword(s):
Machine Learning, Multiclass Classifiers, Supervised Classification, Somatotype.
Related
Ontology
Subjects/Areas/Topics:
Computer Systems in Sports
;
Simulation and Mathematical Modeling
;
Sport Science Research and Technology
Abstract:
System modeling (identification) in complex systems like kinesiological and biological in general is extremely difficult due to the high dimensions of parameters and usually non-linear functional dependencies. Data Science and especially Machine Learning (Deep Learning) algorithms seem to be quite a good tool for analysis and problem-solving in sports today. Data Science (Machine or Deep Learning) algorithms rely on basic use of statistical algorithms, but extend those with models such as Decision tree, K-means clustering, Neural networks, and Reinforcement learning, creating new algorithms that handle input data by predicting outputs that describe correlation relations or predict future states at time points (regression). This study is an attempt to analyze and research applications of machine learning in Sport science - Kinanthropometry related problem of determining somatotype by using the Microsoft Azure Machine Learning platform and comparing several supervised classifier algori
thms (Multiclass Neural Network, Multiclass Decision Forest, Multiclass Decision Jungle and Multiclass Logistic Regression) which were compared versus classical somatotype categorization algorithms with dataset based on the Heath-Carter method Somatotype determination to gain experience and expertise.
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