학술논문
Personalized Learning using Multiple Kernel Models
Document Type
Conference
Author
Source
2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), 2021 Asia-Pacific. :2085-2088 Dec, 2021
Subject
Language
ISSN
2640-0103
Abstract
This paper considers a personalized learning system with a large number of users each trying to learn a desired task. The user learning tasks have similarities as each of the users have common attributes. However, the learning tasks are each slightly different as the tasks are personalized to each learning system. There is also the concern that each learning system may not receive enough data to learn its desired task. To account for this a few canonical learners receive all the data from each personalized learning system. Each personalized learning system then takes a weighted sum of the canonical learners to realize their desired task. Here we will consider learning via kernel methods.