Nicolas RAGOT

About

Nicolas Ragot recieved his Ph.D. degree in computer science in 2003 from IRISA lab, Rennes University (France). Since 2005, he joined the Computer Science Lab (LI EA 6300) in the RFAI group of Université de Tours (France) where he is Professor at Polytech Tours (French engineering school). His main research area is Pattern Recognition and machine learning, currently focused on spatio-temporal data and time series, with applications on Environment (digital twins) and Health. Previously, he worked also on Document analysis (online signature recognition, robust and adaptative OCR systems based on HMM, OCR control and defects detection).

Employment

• Université de Tours Laboratoire d'Informatique Fondamentale et Appliquée de Tours Professor
  2023 - Present

Education

• Université de Rennes 1 Doctorat
  2000 - 2003

Projects & Funding

Projects & funding information is unavailable.

Publications (36)

• Empreinte carbone des IA : regards croisés entre performances et nécessité d'adaptation
  Université de Tours 2026 DOI: 10.60527/jpfa-q866
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• Attention-based Experience Replay for Knowledge Distillation: an Efficient Model-Agnostic Continual Learning strategy for Multivariate Time Series Forecasting
  2026 DOI: 10.2139/ssrn.7050287
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• Continual Learning for Time Series Forecasting: A First Survey
  2024 DOI: 10.3390/engproc2024068049
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• ConvNeXt based semi-supervised approach with consistency regularization for weeds classification
  Expert Systems with Applications 2024 DOI: 10.1016/J.ESWA.2023.122222
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• Efficient Dynamic Texture Classification with Probabilistic Motifs
  Proceedings - International Conference on Pattern Recognition 2022 DOI: 10.1109/ICPR56361.2022.9956557
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• Deep Learning for Document Layout Generation: A First Reproducible Quantitative Evaluation and a Baseline Model
  Lecture Notes in Computer Science 2021 DOI: 10.1007/978-3-030-86334-0_2
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• Trajectory Extraction and Deep Features for Classification of Liquid-gas Flow under the Context of Forced Oscillation
  Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) 2020 DOI: 10.5220/0008870700170026
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• Comparative study of conventional time series matching techniques for word spotting
  Pattern Recognition 2018 DOI: 10.1016/J.PATCOG.2017.07.011
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• A multi-one-class dynamic classifier for adaptive digitization of document streams
  International Journal on Document Analysis and Recognition 2017 DOI: 10.1007/S10032-017-0286-6
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• Flexible Sequence Matching technique: An effective learning-free approach for word spotting
  Pattern Recognition 2016 DOI: 10.1016/J.PATCOG.2016.05.011
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