caa a22 4500 605469393 CHVBK 20210128100322.0 cr unu---uuuuu 210128e20150301xx s 000 0 eng 10.1007/s00500-014-1304-y doi (NATIONALLICENCE)springer-10.1007/s00500-014-1304-y Time series prediction using sparse regression ensemble based on $$\ell _2$$ ℓ 2 - $$\ell _1$$ ℓ 1 problem [Elektronische Daten] [Li Zhang, Wei-Da Zhou] Sparse regression ensemble (SRE) is to sparsely combine the outputs of multiple learners using a sparse weight vector. This paper deals with SRE based on the $$\ell _2$$ ℓ 2 - $$\ell _1$$ ℓ 1 problem and applies it to time series prediction problems. The $$\ell _2$$ ℓ 2 - $$\ell _1$$ ℓ 1 problem consists of $$\ell _2$$ ℓ 2 -norm and $$\ell _1$$ ℓ 1 -norm regularization terms, where the former denotes the total ensemble empirical risk, and the latter represents the ensemble complexity. Thus, the goal is both to minimize the total ensemble training error and control the ensemble complexity. Experiments on real-world data for regression and time series prediction are given. Springer-Verlag Berlin Heidelberg, 2014 Sparse regression ensemble nationallicence $$\ell _2$$ ℓ 2 - $$\ell _1$$ ℓ 1 problem nationallicence Regression tasks nationallicence Time series prediction nationallicence Zhang Li School of Computer Science and Technology and Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, 215006, Suzhou, Jiangsu, China aut Zhou Wei-Da AI Speech Ltd, 215123, Suzhou, Jiangsu, China aut Soft Computing Springer Berlin Heidelberg 19/3(2015-03-01), 781-792 1432-7643 19:3<781 2015 19 500 https://doi.org/10.1007/s00500-014-1304-y 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/s00500-014-1304-y text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Zhang Li School of Computer Science and Technology and Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, 215006, Suzhou, Jiangsu, China aut NATIONALLICENCE 700 1- Zhou Wei-Da AI Speech Ltd, 215123, Suzhou, Jiangsu, China aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/3(2015-03-01), 781-792 1432-7643 19:3<781 2015 19 500