Learning Rates for Multi-task Regularization Networks

• URL: http://arxiv.org/abs/2104.00453v1
• Date: Thu, 1 Apr 2021 13:10:29 GMT
• Title: Learning Rates for Multi-task Regularization Networks
• Authors: Jie Gui and Haizhang Zhang
• Abstract summary: Multi-task learning is an important trend in machine learning in facing the era of artificial intelligence and big data. We present mathematical 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. It reveals that the generalization ability of multi-task learning algorithms is indeed affected as the number of tasks increases.
• Score: 7.799917891986168
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 mathematical 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 regularization 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.

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