Graph-Induced Geodesics Approximation for Non-Euclidian K-Means

Autor: Hervé Frezza-Buet

Publikováno v: ESANN 2022-European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
ESANN 2022-European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Oct 2022, Bruges, Belgium

International audience; In this paper, an adaptation of the k-means algorithm and related methods to non-Euclidian topology is presented. The paper introduces a rationale for approximating the geodesics of that topology, as well as a learning rule th

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::40ab5d6cec81e5997615c107ec912b98
https://centralesupelec.hal.science/hal-03823878/document

Look and Feel What and How Recurrent Self-Organizing Maps Learn

Autor: Jérémy Fix, Hervé Frezza-Buet

Publikováno v: Advances in Intelligent Systems and Computing ISBN: 9783030196417
WSOM
Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization, WSOM 19
Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization, WSOM 19, 976, pp.3-12, 2020, Advances in Intelligent Systems and Computing, 978-3-030-19641-7. ⟨10.1007/978-3-030-19642-4_1⟩

International audience; This paper introduces representations and measurements for revealing the inner self-organization that occurs in a 1D recurrent self-organizing map. Experiments show the incredible richness and robustness of an extremely simple

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4f704923df3023ad4c58fc67d2a89394
https://doi.org/10.1007/978-3-030-19642-4_1

Following non-stationary distributions by controlling the vector quantization accuracy of a growing neural gas network

Autor: Hervé Frezza-Buet

Publikováno v: Neurocomputing
Neurocomputing, Elsevier, 2008, 71 (7-9), pp.1191-1202. ⟨10.1016/j.neucom.2007.12.024⟩

International audience; In this paper, an original method extended from growing neural gas (GNG-T) [B. Fritzke, A growing neural gas network learns topologies, in: G. Tesauro, D.S. Touretzky, T.K. Leen (Eds.), Advances in Neural Information Processin

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::eb1db7462a4dd69feb34be88dfd37dbc
https://doi.org/10.1016/j.neucom.2007.12.024

Tracking non-stationary dynamical system phase using multi-map and temporal self-organizing architecture

Autor: Bassem Khouzam, Hervé Frezza-Buet

Publikováno v: Proceedings of Third World Congress on Nature and Biologically Inspired Computing
NaBIC 2011
NaBIC 2011, Oct 2011, Salamanca, Spain. pp.163-168, ⟨10.1109/NaBIC.2011.6089453⟩
NaBIC

International audience; This paper presents a multi-map recurrent neural architecture, exhibiting self-organization to deal with the partial observations of the phase of some dynamical system. The architecture captures the dynamics of the system by b

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::af6c944f87f8e0c6caf56bdebf8088b8
https://hal-supelec.archives-ouvertes.fr/hal-00652312/file/KhouzamNaBIC11.pdf

Dynamic Neural Field Optimization using the Unscented Kalman Filter

Autor: Matthieu Geist, Jérémy Fix, Olivier Pietquin, Hervé Frezza-Buet

Publikováno v: Proceedings of the IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain
CCMB 2011
CCMB 2011, Apr 2011, Paris, France. 7 p., ⟨10.1109/CCMB.2011.5952113⟩
CCMB

International audience; Dynamic neural fields have been proposed as a continuous model of a neural tissue. When dynamic neural fields are used in practical applications, the tuning of their parameters is a challenging issue that most of the time reli

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3c8911014d90ad0d7ced7149eca1c692
https://hal-supelec.archives-ouvertes.fr/hal-00618117/file/CCMB_2011_JFMGOPHFB.pdf