Hokkaido University Collection of Scholarly and Academic Papers >
Graduate School of Information Science and Technology / Faculty of Information Science and Technology >
Peer-reviewed Journal Articles, etc >
Transfer learning based on the observation probability of each attribute
Title: | Transfer learning based on the observation probability of each attribute |
Authors: | Suzuki, Masahiro Browse this author | Sato, Haruhiko Browse this author →KAKEN DB | Oyama, Satoshi Browse this author →KAKEN DB | Kurihara, Masahito Browse this author →KAKEN DB |
Keywords: | transfer learning | attributes | multiclass classification | incremental learning | generative model |
Issue Date: | 2014 |
Publisher: | IEEE (Institute of Electrical and Electronics Engineers) |
Citation: | 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC), ISBN: 978-1-4799-3840-7 |
Start Page: | 3627 |
End Page: | 3631 |
Publisher DOI: | 10.1109/SMC.2014.6974493 |
Abstract: | Machine learning is the basis of important advances in artificial intelligence. Unlike the general methods of machine learning, which use the same tasks for training and testing, the method of transfer learning uses different tasks to learn a new task. Among the various transfer learning algorithms in the literature, we focus on the attribute-based transfer learning. This algorithm realizes transfer learning by introducing attributes and transferring the results of training to another task with the common attributes. However, the existing method does not consider the frequency in which each attribute appears in feature vectors (called the observation probability). In this paper, we present a generative model with the observation probability. By the experiments, we show that the proposed method has achieved a higher accuracy rate than the existing method. Moreover, we see that it makes possible the incremental learning that was impossible in the existing method. |
Conference Name: | IEEE International Conference on Systems, Man, and Cybernetics (SMC) |
Conference Sequence: | 2014 |
Conference Place: | San Diego |
Rights: | © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Type: | proceedings (author version) |
URI: | http://hdl.handle.net/2115/66068 |
Appears in Collections: | 情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
|
Submitter: 小山 聡
|