Zobrazeno 1 - 10
of 59
pro vyhledávání: '"Florent Chatelain"'
Publikováno v:
BMC Cancer, Vol 22, Iss 1, Pp 1-16 (2022)
Abstract Background Prediction of patient survival from tumor molecular ‘-omics’ data is a key step toward personalized medicine. Cox models performed on RNA profiling datasets are popular for clinical outcome predictions. But these models are ap
Externí odkaz:
https://doaj.org/article/30b3c245ac8e4908a575d64d5719bc9e
Publikováno v:
Journal of Probability and Statistics, Vol 2013 (2013)
This paper derives new closed-form expressions for the masses of negative multinomial distributions. These masses can be maximized to determine the maximum likelihood estimator of its unknown parameters. An application to polarimetric image processin
Externí odkaz:
https://doaj.org/article/7b346a8f58ba49bfa2a46faa7692f480
Publikováno v:
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
Publikováno v:
Genes; Volume 13; Issue 12; Pages: 2275
(1)Backgroundtumor profiling enables patient survival prediction. The two essential parameters to be calibrated when designing a study based on tumor profiles from a cohort are the sequencing depth of RNA-seq technology and the number of patients. Th
This paper introduces a random matrix framework to analyze the trade-off between performance and complexity in a class ofmachine learning algorithms, under a high-dimensional data regime. More precisely, we analyse the spectral properties of Kij , fo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______166::6ea6f7efc907918303fc957ad050139c
https://hal.science/hal-03868290
https://hal.science/hal-03868290
Publikováno v:
MLSP 2020-IEEE 30th International Workshop on Machine Learning for Signal Processing
MLSP 2020-IEEE 30th International Workshop on Machine Learning for Signal Processing, Sep 2020, Espoo (virtual), Finland. ⟨10.1109/MLSP49062.2020.9231568⟩
MLSP
MLSP 2020-IEEE 30th International Workshop on Machine Learning for Signal Processing, Sep 2020, Espoo (virtual), Finland. ⟨10.1109/MLSP49062.2020.9231568⟩
MLSP
This article introduces a random matrix framework for the analysis of the trade-off between performance and complexity in a class of machine learning algorithms, under a large dimensional data $X= [x_{1},\ \ldots,\ x_{n}]\in \mathbb{R}^{p\times n}$ r
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a3ad38b76dd14e9cf72387b509c326e7
https://hal.archives-ouvertes.fr/hal-02961057/file/MLSP_2020_wo_copyright.pdf
https://hal.archives-ouvertes.fr/hal-02961057/file/MLSP_2020_wo_copyright.pdf
Publikováno v:
Mechanical Systems and Signal Processing
Mechanical Systems and Signal Processing, Elsevier, 2020, 139, pp.106561. ⟨10.1016/j.ymssp.2019.106561⟩
Mechanical Systems and Signal Processing, Elsevier, 2020, 139, pp.106561. ⟨10.1016/j.ymssp.2019.106561⟩
International audience; The use of Instantaneous Angular Speed (IAS) in condition monitoring of rotating machines is an appealing alternative to traditional approaches such as those based on accelerometers: the direct angular sampling characteristic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::055949ddc4a8bee63f1c197fc9f32f5a
https://hal.archives-ouvertes.fr/hal-02408146/document
https://hal.archives-ouvertes.fr/hal-02408146/document
MotivationPrediction of patient survival from tumor molecular ‘omics’ data is a key step toward personalized medicine. With this aim, the databases available are growing, with the collection of various ‘omics’ characterizations of patient tum
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ff784d4f921e158aca90abd2ee4a05f6
Robust Control of Varying Weak Hyperspectral Target Detection With Sparse Nonnegative Representation
Publikováno v:
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing, Institute of Electrical and Electronics Engineers, 2017, 65 (13), pp.3538-3550. ⟨10.1109/TSP.2017.2688965⟩
IEEE Transactions on Signal Processing, 2017, 65 (13), pp.3538-3550. ⟨10.1109/TSP.2017.2688965⟩
IEEE Transactions on Signal Processing, Institute of Electrical and Electronics Engineers, 2017, 65 (13), pp.3538-3550. ⟨10.1109/TSP.2017.2688965⟩
IEEE Transactions on Signal Processing, 2017, 65 (13), pp.3538-3550. ⟨10.1109/TSP.2017.2688965⟩
International audience; In this study, a multiple-comparison approach is developed for detecting faint hyperspectral sources. The detection method relies on a sparse and non-negative representation on a highly coherent dictionary to track a spatially
Autor:
Nicolas Le Bihan, Florent Chatelain
Publikováno v:
ICASSP 2019-IEEE International Conference on Acoustics, Speech and Signal Processing
ICASSP 2019-IEEE International Conference on Acoustics, Speech and Signal Processing, May 2019, Brighton, United Kingdom. pp.8509-8513, ⟨10.1109/ICASSP.2019.8683518⟩
ICASSP
ICASSP 2019-IEEE International Conference on Acoustics, Speech and Signal Processing, May 2019, Brighton, United Kingdom. pp.8509-8513, ⟨10.1109/ICASSP.2019.8683518⟩
ICASSP
Improperness testing for complex-valued vectors and processes has been of interest lately due to the potential applications of complex-valued time series analysis in several research areas. This paper provides exact distribution characterization of t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::17a6d0bf4701ef0e604ed4fd7f1f4bea
https://hal.archives-ouvertes.fr/hal-02148428/document
https://hal.archives-ouvertes.fr/hal-02148428/document