Kernels for multi-task learning This paper provides a foundation for multi-task learning using reproducing kernel Hilbert spaces of vector-valued functions. In this setting, the kernel is a matrix-valued function. Some explicit examples will be described which go beyon... CA Micchelli,MA Pontil ...
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 ...
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...
for 2.0 kernel * Copyright (C) 1998 Kenneth Albanowski <kjahds@kjahds.com> * JAN/99 -- coded full program relocation (gerg@snapgear.com) */ #define pr_fmt(fmt) KBUILD_MODNAME ": " fmt #include <linux/kernel.h> #include <linux/sched.h> #include <linux/sched/task_stack.h> #...
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 ...
Versatility and Flexibility: With a dual boot setup, you have the flexibility to switch between Linux Mint and Windows based on your needs. This allows you to leverage the strengths of both operating systems and choose the one that is most suitable for a particular task or application. ...
c Springer-Verlag Berlin Heidelberg 2005 Ensemble Learning with Supervised Kernels 401 has been trained either for a classification or a regression task. Ensembles of kernel-based learners are described next, followed by illustrations and experi- mentation with the supervised kernel. We use both ...
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 ...