Time series prediction using sparse regression ensemble based on $$\ell _2$$ ℓ 2 - $$\ell _1$$ ℓ 1 problem
| LEADER | caa a22 4500 | ||
|---|---|---|---|
| 001 | 605469393 | ||
| 003 | CHVBK | ||
| 005 | 20210128100322.0 | ||
| 007 | cr unu---uuuuu | ||
| 008 | 210128e20150301xx s 000 0 eng | ||
| 024 | 7 | 0 | |a 10.1007/s00500-014-1304-y |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s00500-014-1304-y | ||
| 245 | 0 | 0 | |a Time series prediction using sparse regression ensemble based on $$\ell _2$$ ℓ 2 - $$\ell _1$$ ℓ 1 problem |h [Elektronische Daten] |c [Li Zhang, Wei-Da Zhou] |
| 520 | 3 | |a 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. | |
| 540 | |a Springer-Verlag Berlin Heidelberg, 2014 | ||
| 690 | 7 | |a Sparse regression ensemble |2 nationallicence | |
| 690 | 7 | |a $$\ell _2$$ ℓ 2 - $$\ell _1$$ ℓ 1 problem |2 nationallicence | |
| 690 | 7 | |a Regression tasks |2 nationallicence | |
| 690 | 7 | |a Time series prediction |2 nationallicence | |
| 700 | 1 | |a Zhang |D Li |u School of Computer Science and Technology and Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, 215006, Suzhou, Jiangsu, China |4 aut | |
| 700 | 1 | |a Zhou |D Wei-Da |u AI Speech Ltd, 215123, Suzhou, Jiangsu, China |4 aut | |
| 773 | 0 | |t Soft Computing |d Springer Berlin Heidelberg |g 19/3(2015-03-01), 781-792 |x 1432-7643 |q 19:3<781 |1 2015 |2 19 |o 500 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s00500-014-1304-y |q text/html |z Onlinezugriff via DOI |
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 900 | 7 | |a Metadata rights reserved |b Springer special CC-BY-NC licence |2 nationallicence | |
| 908 | |D 1 |a research-article |2 jats | ||
| 949 | |B NATIONALLICENCE |F NATIONALLICENCE |b NL-springer | ||
| 950 | |B NATIONALLICENCE |P 856 |E 40 |u https://doi.org/10.1007/s00500-014-1304-y |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Zhang |D Li |u School of Computer Science and Technology and Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, 215006, Suzhou, Jiangsu, China |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Zhou |D Wei-Da |u AI Speech Ltd, 215123, Suzhou, Jiangsu, China |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t Soft Computing |d Springer Berlin Heidelberg |g 19/3(2015-03-01), 781-792 |x 1432-7643 |q 19:3<781 |1 2015 |2 19 |o 500 | ||