Online Multi-Task Learning with Recursive Least Squares and Recursive Kernel Methods
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| Publicado en: | arXiv.org (Mar 17, 2024), p. n/a |
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Cornell University Library, arXiv.org
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| Acceso en línea: | Citation/Abstract Full text outside of ProQuest |
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| 001 | 2847146587 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 2847146587 | ||
| 045 | 0 | |b d20240317 | |
| 100 | 1 | |a Lencione, Gabriel R | |
| 245 | 1 | |a Online Multi-Task Learning with Recursive Least Squares and Recursive Kernel Methods | |
| 260 | |b Cornell University Library, arXiv.org |c Mar 17, 2024 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a This paper introduces two novel approaches for Online Multi-Task Learning (MTL) Regression Problems. We employ a high performance graph-based MTL formulation and develop two alternative recursive versions based on the Weighted Recursive Least Squares (WRLS) and the Online Sparse Least Squares Support Vector Regression (OSLSSVR) strategies. Adopting task-stacking transformations, we demonstrate the existence of a single matrix incorporating the relationship of multiple tasks and providing structural information to be embodied by the MT-WRLS method in its initialization procedure and by the MT-OSLSSVR in its multi-task kernel function. Contrasting the existing literature, which is mostly based on Online Gradient Descent (OGD) or cubic inexact approaches, we achieve exact and approximate recursions with quadratic per-instance cost on the dimension of the input space (MT-WRLS) or on the size of the dictionary of instances (MT-OSLSSVR). We compare our online MTL methods to other contenders in a real-world wind speed forecasting case study, evidencing the significant gain in performance of both proposed approaches. | |
| 653 | |a Learning | ||
| 653 | |a Support vector machines | ||
| 653 | |a Kernel functions | ||
| 653 | |a Least squares | ||
| 653 | |a Wind speed | ||
| 700 | 1 | |a Von Zuben, Fernando J | |
| 773 | 0 | |t arXiv.org |g (Mar 17, 2024), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2847146587/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2308.01938 |