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Transfer learning based on the observation probability of each attribute

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Please use this identifier to cite or link to this item:http://hdl.handle.net/2115/66068

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

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