TY - CHAP
T1 - Predictive modeling without notion of time
AU - Hoogendoorn, Mark
AU - Funk, Burkhardt
PY - 2018
Y1 - 2018
N2 - Supervised learning approaches that do not explicitly take the time component into account are briefly discussed in this chapter. The approaches explained include feedforward neural networks, support vector machines, k-nearest neighbor, decision trees, naïve bayes and ensembles. Guidelines are provided on how to apply these algorithms to quantified self data, including the learning setup (e.g. learning for single users or across multiple users) and other practical considerations such as feature selection and regularization. Data stream mining approaches for predictive modeling are also briefly discussed.
AB - Supervised learning approaches that do not explicitly take the time component into account are briefly discussed in this chapter. The approaches explained include feedforward neural networks, support vector machines, k-nearest neighbor, decision trees, naïve bayes and ensembles. Guidelines are provided on how to apply these algorithms to quantified self data, including the learning setup (e.g. learning for single users or across multiple users) and other practical considerations such as feature selection and regularization. Data stream mining approaches for predictive modeling are also briefly discussed.
UR - https://www.scopus.com/pages/publications/85030663789
UR - https://www.scopus.com/inward/citedby.url?scp=85030663789&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-66308-1_7
DO - 10.1007/978-3-319-66308-1_7
M3 - Chapter
AN - SCOPUS:85030663789
SN - 9783319663074
SN - 9783319882154
T3 - Cognitive Systems Monographs
SP - 123
EP - 165
BT - Machine Learning for the Quantified Self
PB - Springer/Verlag
ER -
