変化検知のための統計的機械学習

Material type: 博士論文

Author: LIU, SONGほか

Publisher: -

Publication date: 2014-03

Material Format: Digital

Capacity, size, etc.: -

Name of awarding university/degree: 東京工業大学,博士(工学)

View All

Notes on use

Note (General)：: In this thesis, we focus on unsupervised change detection, and propose two novel approaches in distributional and structural change detection respecti...

Search by Bookstore

Holdings of Libraries in Japan

This page shows libraries in Japan other than the National Diet Library that hold the material.

List of Cooperating Institutions and Databases

Please contact your local library for information on how to use materials or whether it is possible to request materials from the holding libraries.

other

Tokyo Tech Research Repository
Digital
You can check the holdings of institutions and databases with which 学術機関リポジトリデータベース（IRDB）（機関リポジトリ） is linked at the site of 学術機関リポジトリデータベース（IRDB）（機関リポジトリ）.
Tokyo Tech Research Repository

Search by Bookstore

Bibliographic Record

You can check the details of this material, its authority (keywords that refer to materials on the same subject, author's name, etc.), etc.

Digital

Material Type: 博士論文
Title: 変化検知のための統計的機械学習
Author/Editor: LIU, SONG
Liu, Song
Author Heading: LIU, SONG
Liu, Song
Publication Date: 2014-03
Publication Date (W3CDTF): 2014-03
Alternative Title: Statistical Machine Learning Approaches to Change Detection
Degree grantor/type: 東京工業大学
Date Granted: 2014-03-26
Dissertation Number: 甲第9555号
Degree Type: 博士(工学)
Text Language Code: eng
Subject Heading: Change Detection, Machine Learning, Density Ratio Estimation
Target Audience: 一般
Note (General): In this thesis, we focus on unsupervised change detection, and propose two novel approaches in distributional and structural change detection respectively. First, we propose a statistical change-point detection algorithm based on non-parametric divergence estimation between time-series samples from two retrospective segments. Our method uses the relative Pearson divergence as a divergence measure, and it is accurately estimated by a method of density ratio estimation. Second, we propose a method for detecting changes in Markov network structure between two sets of samples. Instead of naively fitting two Markov network models separately to the two data sets and figuring out their difference, we directly learn the network structure change by estimating the ratio of Markov network models. The usefulness of both methods is demonstrated by experiments. The results show that the proposed methods can successfully capture the changes of patterns in many real-world applications.
identifier:oai:t2r2.star.titech.ac.jp:50230928
Format (IMT): application/pdf
Source: http://t2r2.star.titech.ac.jp/rrws/file/CTT100666815/ATD100000413/main.pdf （fulltext）
http://t2r2.star.titech.ac.jp/rrws/file/CTT100666815/ATD100000413/main.pdf
Access Restrictions: インターネット公開
Data Provider (Database): 国立情報学研究所 : 学術機関リポジトリデータベース（IRDB）（機関リポジトリ）
https://irdb.nii.ac.jp
Original Data Provider (Database): 東京工業大学 : 東京工業大学リサーチリポジトリ