caa a22 4500 605478325 CHVBK 20210128100405.0 cr unu---uuuuu 210128e20150901xx s 000 0 eng 10.1007/s10994-015-5513-0 doi (NATIONALLICENCE)springer-10.1007/s10994-015-5513-0 Jain Brijnesh Technische Universität Berlin, Berlin, Germany aut Generalized gradient learning on time series [Elektronische Daten] [Brijnesh Jain] The majority of machine learning algorithms assumes that objects are represented as vectors. But often the objects we want to learn on are more naturally represented by other data structures such as sequences and time series. For these representations many standard learning algorithms are unavailable. We generalize gradient-based learning algorithms to time series under dynamic time warping. To this end, we introduce elastic functions, which extend functions on Euclidean spaces to time series spaces. Necessary conditions are sketched under which generalized gradient learning on time series is consistent. Specifically, four linear classifiers are extended to time series under dynamic time warping and applied to benchmark datasets. Results indicate that generalized gradient learning via elastic functions have the potential to complement the state-of-the-art in pattern recognition on time series. The Author(s), 2015 Time series nationallicence Elastic distance measures nationallicence Dynamic time warping nationallicence Linear classifiers nationallicence Generalized gradient methods nationallicence Machine Learning Springer US; http://www.springer-ny.com 100/2-3(2015-09-01), 587-608 0885-6125 100:2-3<587 2015 100 10994 https://doi.org/10.1007/s10994-015-5513-0 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10994-015-5513-0 text/html Onlinezugriff via DOI NATIONALLICENCE 100 1- Jain Brijnesh Technische Universität Berlin, Berlin, Germany aut NATIONALLICENCE 773 0- Machine Learning Springer US; http://www.springer-ny.com 100/2-3(2015-09-01), 587-608 0885-6125 100:2-3<587 2015 100 10994