.png){kind=icon}
返回
Learning rates for multi-task regularization networks
.png){kind=icon}
OA
AI
DOI：10.1016/j.neucom.2021.09.031.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Multi-task learning is an important trend of machine learning in facing the era of artificial intelligence and big data. Despite a large amount of researches on learning rate estimates of various single-task machine learning algorithms, there is little parallel work for multi-task learning. We present mathemat-ical analysis on the learning rate estimate of multi-task learning based on the theory of vector-valued reproducing kernel Hilbert spaces and matrix-valued reproducing kernels. For the typical multi-task reg-ularization networks, an explicit learning rate dependent both on the number of sample data and the number of tasks is obtained. It reveals that the generalization ability of multi-task learning algorithms is indeed affected as the number of tasks increases. (c) 2021 Elsevier B.V. All rights reserved.
Keyword:
Vector-valued reproducing kernel Hilbert
spaces
Multi-task learning
Matrix-valued reproducing kernels
Learning rates
Regularization networks
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
机构
引用论文
.png){kind=icon}
分享收藏
Combination of DCE-MRI and NME-DWI via Deep Neural Network for Predicting Breast Cancer Molecular Subtypes基于深度神经网络将DCE-MRI与NME-DWI相结合预测乳腺癌分子亚型
PREAI
分享收藏
查看更多内容