Universal Kernels for MultiTask Learning with reproducing kernel Hilbert spaces HK of functions from an input space into a Hilbert space Y, an environment appropriate for multi-task learning. The... A Caponnetto 被引量: 0发表: 2007年 Kernels for multi-task learning This paper provides a found...
Motivated by the importance of kernel-based methods for multi-task learning, we provide here a complete characterization of multi-task finite rank kernels in terms of the positivity of what we call its associated characteristic operator. Consequently, we are led to establishing that every continuous...
Many kernel based methods for multi-task learning have been proposed, which leverage relations among tasks to enhance the overall learning accuracies. Most of the methods assume that the learning tasks share the same kernel [e.g., 13], which could limit their applications because in practice...
In this paper we are concerned with reproducing kernel Hilbert spaces H-K of functions from an input space into a Hilbert space Y, an environment appropriate for multi-task learning. The reproducing kernel K associated to H-K has its values as operators on Y. Our primary goal here is to ...
Also, there is no systematic methodology that allows for an a priori estimation of their optimal values. In fact, given a classification task, picking the best values for these variables is a nontrivial model selection problem that needs either an exhaustive search over the space of hyper-...
Combined electricity-heat-cooling-gas load forecasting model for integrated energy system based on multi-task learning and least square support vector machine 2020, Journal of Cleaner Production Citation Excerpt : In the IES, different energy subsystems achieve strong couplings, and their energy conversi...
(struct flat_hdr); /* the real code len */ /* The main program needs a little extra setup in the task structure */ current->mm->start_code = start_code; current->mm->end_code = end_code; current->mm->start_data = datapos; current->mm->end_data = datapos + data_len; /*...
Our results demonstrate that SCGK achieves the state-of-the-art performance on the task of semantic relation extraction. 展开 关键词: Relation Extraction Graph Kernels Semi-supervised Learning Natural Language Processing DOI: 10.1137/1.9781611972818.44 被引量: 2 ...
Kernel learning Time series extrapolation 1. Introduction Gaussian Processes (GPs) [1] are one of the most used techniques in Machine Learning for regression and classification tasks. Furthermore, they have also been applied to optimization tasks under the umbrella of Bayesian optimization [2]. A ...
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