Multi-Source Change-Point Detection over Local Observation Models-Reference-Cited by-同舟云学术

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Multi-Source Change-Point Detection over Local Observation Models

Published:2023-02 Issue: Volume:134 Page:109116
ISSN:0031-3203
Container-title:Pattern Recognition
language:en
Short-container-title:Pattern Recognition

Author:

Romero-Medrano Lorena^ORCID,Artés-Rodríguez Antonio

Funder

Horizon 2020

European Research Council

Agencia Estatal de Investigación

Ministry of Communications and Information, Singapore

Horizon 2020 Framework Programme

Federación Española de Enfermedades Raras

Comunidad de Madrid

Université Pierre et Marie Curie

Universidad de Zaragoza

European Commission

Ministerio de Ciencia, Innovación y Universidades

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Signal Processing,Software

Reference22 articles.

1. R. P. Adams, D. J. MacKay, Bayesian online changepoint detection, 2007.

2. Combining continuous smartphone native sensors data capture and unsupervised data mining techniques for behavioral changes detection: a case series of the evidence-based behavior (eB2) study;Berrouiguet;JMIR mHealth uHealth,2018

3. Uses of electronic health records for public health surveillance to advance public health;Birkhead;Annu. Rev. Public Health,2015

4. Principles of time–frequency feature extraction for change detection in non-stationary signals: applications to newborn EEG abnormality detection;Boashash;Pattern Recognit.,2015

5. G.J.J. van den Burg, C.K.I. Williams, An evaluation of change point detection algorithms, 2020. arXiv:2003.06222.

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