Sparse Decomposition of Heart Rate Using a Bernoulli-Gaussian Model: Application to Sleep Apnoea Detection

Autor: Bruno H. Muller, Régis Lengellé

Publikováno v: Sensors, Vol 23, Iss 7, p 3743 (2023)

In this paper, we propose a sparse decomposition of the heart rate during sleep with an application to apnoea–RERA detection. We observed that the tachycardia following an apnoea event has a quasi-deterministic shape with a random amplitude. Accord

Externí odkaz: https://doaj.org/article/cef6fd44bd204e2195f4efb425ea4202

Domain Adaptation Transfer Learning by Kernel Representation Adaptation

Autor: Xiaoyi Chen, Régis Lengellé

Publikováno v: Lecture Notes in Computer Science ISBN: 9783319936468
ICPRAM (Revised Selected Papers)
Lecture Notes In Computer Science
6th International Pattern Recognition Applications and Methods Conference, ICPRAM 2017
De Marsico M., di Baja G., Fred A. Lecture Notes In Computer Science, 10857, pp.45-61, 2018, ⟨10.1007/978-3-319-93647-5_3⟩

International audience; Domain adaptation, where no labeled target data is available, is a challenging task. To solve this problem, we first propose a new SVM based approach with a supplementary MaximumMean Discrepancy (MMD)-like constraint. With thi

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0fb1461a5c9b440f0c04967d536cfa4e
https://doi.org/10.1007/978-3-319-93647-5_3

Domain Adaptation Transfer Learning by SVM Subject to a Maximum-Mean-Discrepancy-like Constraint

Autor: Xiaoyi Chen, Régis Lengellé

Publikováno v: ICPRAM
Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2017
6th International Conference on Pattern Recognition Applications and Methods
6th International Conference on Pattern Recognition Applications and Methods, Feb 2017, Porto, Portugal. pp.89-95, ⟨10.5220/0006119900890095⟩

International audience; This paper is a contribution to solving the domain adaptation problem where no labeled target data is available. A new SVM approach is proposed by imposing a zero-valued Maximum Mean Discrepancy-like constraint. This heuristic

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3dfbe4cd1fda251a43ba9860715c715d
https://doi.org/10.5220/0006119900890095

Kernel Hierarchical Agglomerative Clustering - Comparison of Different Gap Statistics to Estimate the Number of Clusters

Autor: Régis Lengellé, Nicolas Lefebvre, Na Li

Publikováno v: Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods, ICPRAM 2014
International Conference on Pattern Recognition Applications and Methods
International Conference on Pattern Recognition Applications and Methods, Mar 2014, Angers, France. pp.255-262, ⟨10.5220/0004828202550262⟩

Clustering algorithms, as unsupervised analysis tools, are useful for exploring data structure and have owned great success in many disciplines. For most of the clustering algorithms like k-means, determining the number of the clusters is a crucial s

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5b0eff68728e98af2220d08fb316a24c
https://hal-utt.archives-ouvertes.fr/hal-02861450

Performance analysis of kernel adaptive filters based on RLS algorithm

Autor: Ibtissam Constantin, Régis Lengellé

Publikováno v: 2013 25th International Conference on Microelectronics (ICM)
2013 25th International Conference on Microelectronics (ICM), Dec 2013, Beirut, Lebanon. pp.1-4, ⟨10.1109/ICM.2013.6734965⟩

International audience; The design of adaptive nonlinear filters has sparked a great interest in the machine learning community. The present paper aims to present some recent developments in nonlinear adaptive filtering. We present an in-depth analys

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e066a6f7dd24b53a275f401225736200
https://doi.org/10.1109/icm.2013.6734965