Zobrazeno 1 - 10
of 20
pro vyhledávání: '"Paul von Bünau"'
Autor:
Elena Williams, Manuel Kienast, Evelyn Medawar, Janis Reinelt, Alberto Merola, Sophie Anne Ines Klopfenstein, Anne Rike Flint, Patrick Heeren, Akira-Sebastian Poncette, Felix Balzer, Julian Beimes, Paul von Bünau, Jonas Chromik, Bert Arnrich, Nico Scherf, Sebastian Niehaus
Publikováno v:
JMIR Medical Informatics, Vol 11, p e43847 (2023)
BackgroundIncreasing digitalization in the medical domain gives rise to large amounts of health care data, which has the potential to expand clinical knowledge and transform patient care if leveraged through artificial intelligence (AI). Yet, big dat
Externí odkaz:
https://doaj.org/article/149dc0feb56b4e4caf306e735d26cc24
FHIR-DHP: A standardized clinical data harmonisation pipeline for scalable AI application deployment
Autor:
Elena Williams, Manuel Kienast, Evelyn Medawar, Janis Reinelt, Alberto Merola, Sophie Anne Ines Klopfenstein, Anne Rike Flint, Patrick Heeren, Akira-Sebastian Poncette, Felix Balzer, Julian Beimes, Paul von Bünau, Jonas Chromik, Bert Arnrich, Nico Scherf, Sebastian Niehaus
Publikováno v:
medRxiv
BackgroundIncreasing digitalisation in the medical domain gives rise to large amounts of healthcare data which has the potential to expand clinical knowledge and transform patient care if leveraged through artificial intelligence (AI). Yet, big data
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::78e22e5c55b7d8af7f41ec24f473630f
https://hdl.handle.net/21.11116/0000-000B-CA58-F
https://hdl.handle.net/21.11116/0000-000B-CA58-F
Autor:
Elena Williams, Manuel Kienast, Evelyn Medawar, Janis Reinelt, Alberto Merola, Sophie Anne Ines Klopfenstein, Anne Rike Flint, Patrick Heeren, Akira-Sebastian Poncette, Felix Balzer, Julian Beimes, Paul von Bünau, Jonas Chromik, Bert Arnrich, Nico Scherf, Sebastian Niehaus
BACKGROUND Increasing digitalization in the medical domain gives rise to large amounts of health care data, which has the potential to expand clinical knowledge and transform patient care if leveraged through artificial intelligence (AI). Yet, big da
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::60b37485bc8fd902054032686aca32af
https://doi.org/10.2196/preprints.43847
https://doi.org/10.2196/preprints.43847
Autor:
Klaus-Robert Müller, Martijn Schreuder, Paul von Bünau, Carmen Vidaurre, Frank C. Meinecke, Jan Saputra Müller
Publikováno v:
Journal of neural engineering. 14(3)
Objective. We present the first generic theoretical formulation of the co-adaptive learning problem and give a simple example of two interacting linear learning systems, a human and a machine. Approach. After the description of the training protocol
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::827e3238fa577ac70561976be355e870
https://pubmed.ncbi.nlm.nih.gov/28224972
https://pubmed.ncbi.nlm.nih.gov/28224972
Autor:
Makoto Yamada, Takafumi Kanamori, Masashi Sugiyama, Taiji Suzuki, Paul von Bünau, Motoaki Kawanabe
Publikováno v:
Neural Networks. 24:183-198
Methods for directly estimating the ratio of two probability density functions have been actively explored recently since they can be used for various data processing tasks such as non-stationarity adaptation, outlier detection, and feature selection
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7f72aec376a157b1bf8b42f7d164889c
https://doi.org/10.1016/j.neunet.2010.10.005
https://doi.org/10.1016/j.neunet.2010.10.005
Autor:
Shinichi Nakajima, Hisashi Kashima, Paul von Bünau, Masashi Sugiyama, Motoaki Kawanabe, Taiji Suzuki
Publikováno v:
Annals of the Institute of Statistical Mathematics. 60(No. 4):699-746
A situation where training and test samples follow different input distributions is called covariate shift. Under covariate shift, standard learning methods such as maximum likelihood estimation are no longer consistent-weighted variants according to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fe1cf175b956ff6cfcfa447d196df497
http://t2r2.star.titech.ac.jp/cgi-bin/publicationinfo.cgi?q_publication_content_number=CTT100575865
http://t2r2.star.titech.ac.jp/cgi-bin/publicationinfo.cgi?q_publication_content_number=CTT100575865
Publikováno v:
Journal of Physics: Conference Series. 699:012021
Non-stationarity in data is an ubiquitous problem in signal processing. The recent stationary subspace analysis procedure (SSA) has enabled to decompose such data into a stationary subspace and a non-stationary part respectively. Algorithmically only
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e683ab8975a6e311ce216dfd7b0cc684
https://doi.org/10.1088/1742-6596/699/1/012021
https://doi.org/10.1088/1742-6596/699/1/012021
Autor:
Yoshinobu Kawahara, Takashi Washio, Kiyohumi Yumoto, Terumasa Tokunaga, Paul von Bünau, Satoshi Hara
Publikováno v:
Neural networks : the official journal of the International Neural Network Society. 33
Non-stationary effects are ubiquitous in real world data. In many settings, the observed signals are a mixture of underlying stationary and non-stationary sources that cannot be measured directly. For example, in EEG analysis, electrodes on the scalp
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::03e5dfeb9c49b774c768eba94ec3e2e2
https://pubmed.ncbi.nlm.nih.gov/22551683
https://pubmed.ncbi.nlm.nih.gov/22551683
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783642217371
ICANN (2)
ICANN (2)
Stationary Subspace Analysis (SSA) is an unsupervised learning method that finds subspaces in which data distributions stay invariant over time. It has been shown to be very useful for studying non-stationarities in various applications. In this pape
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::420799b418647bfd4c7db674cdeb587f
https://doi.org/10.1007/978-3-642-21738-8_51
https://doi.org/10.1007/978-3-642-21738-8_51
Publikováno v:
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference. 2010
Neurophysiological measurements obtained from e.g. EEG or fMRI are inherently non-stationary because the properties of the underlying brain processes vary over time. For example, in Brain-Computer-Interfacing (BCI), deteriorating performance (bitrate
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1873c3fcde23c108607b8fceb04fc754
https://pubmed.ncbi.nlm.nih.gov/21096218
https://pubmed.ncbi.nlm.nih.gov/21096218