{"id":28094,"date":"2025-06-04T12:22:43","date_gmt":"2025-06-04T10:22:43","guid":{"rendered":"https:\/\/www.ummto.dz\/fs\/?p=28094"},"modified":"2025-06-04T14:29:15","modified_gmt":"2025-06-04T12:29:15","slug":"avis-de-soutenance-de-these-de-doctorat-en-sciences-mathematiques-de-mme-abdouche-safia","status":"publish","type":"post","link":"https:\/\/www.ummto.dz\/fs\/avis-de-soutenance-de-these-de-doctorat-en-sciences-mathematiques-de-mme-abdouche-safia\/","title":{"rendered":"Avis de soutenance de th\u00e8se de doctorat en-sciences math\u00e9matiques de Mme. ABDOUCHE Safia"},"content":{"rendered":"

[et_pb_section fb_built=\u00a0\u00bb1″ _builder_version=\u00a0\u00bb4.27.4″ _module_preset=\u00a0\u00bbdefault\u00a0\u00bb global_colors_info=\u00a0\u00bb{}\u00a0\u00bb][et_pb_row _builder_version=\u00a0\u00bb4.27.4″ _module_preset=\u00a0\u00bbdefault\u00a0\u00bb width=\u00a0\u00bb120%\u00a0\u00bb box_shadow_style=\u00a0\u00bbpreset6″ box_shadow_color=\u00a0\u00bb#FFFFFF\u00a0\u00bb global_colors_info=\u00a0\u00bb{}\u00a0\u00bb][et_pb_column type=\u00a0\u00bb4_4″ _builder_version=\u00a0\u00bb4.27.4″ _module_preset=\u00a0\u00bbdefault\u00a0\u00bb global_colors_info=\u00a0\u00bb{}\u00a0\u00bb][et_pb_accordion open_toggle_text_color=\u00a0\u00bb#12876F\u00a0\u00bb _builder_version=\u00a0\u00bb4.27.4″ _module_preset=\u00a0\u00bbdefault\u00a0\u00bb width=\u00a0\u00bb90%\u00a0\u00bb animation_style=\u00a0\u00bbzoom\u00a0\u00bb animation_delay=\u00a0\u00bb300ms\u00a0\u00bb hover_enabled=\u00a0\u00bb0″ box_shadow_style=\u00a0\u00bbpreset6″ box_shadow_horizontal=\u00a0\u00bb-15px\u00a0\u00bb box_shadow_vertical=\u00a0\u00bb-19px\u00a0\u00bb box_shadow_color=\u00a0\u00bb#1FE0BA\u00a0\u00bb global_colors_info=\u00a0\u00bb{}\u00a0\u00bb sticky_enabled=\u00a0\u00bb0″][et_pb_accordion_item title=\u00a0\u00bbAvis de soutenance de th\u00e8se de doctorat en-sciences math\u00e9matiques de Mme. ABDOUCHE Safia\u00a0\u00bb open=\u00a0\u00bbon\u00a0\u00bb open_toggle_text_color=\u00a0\u00bb#0DA0C5″ _builder_version=\u00a0\u00bb4.27.4″ _module_preset=\u00a0\u00bbdefault\u00a0\u00bb hover_enabled=\u00a0\u00bb0″ box_shadow_style=\u00a0\u00bbpreset3″ box_shadow_color=\u00a0\u00bb#1FD7E0″ global_colors_info=\u00a0\u00bb{}\u00a0\u00bb toggle_text_color=\u00a0\u00bb#0DA0C5″ border_width_all=\u00a0\u00bb0px\u00a0\u00bb sticky_enabled=\u00a0\u00bb0″]<\/p>\n

Mme. ABDOUCHE Safia<\/strong><\/span>
Soutiendra publiquement sa th\u00e8se de Doctorat en Sciences en Math\u00e9matiques<\/span>
Intitul\u00e9: << Les mod\u00e8les pr\u00e9dictifs de machine learning. >><\/strong><\/span>
Le: 12\/06\/2025<\/strong> \u00e0 09h30<\/strong><\/span>, \u00e0 la salle de conf\u00e9rences de la facult\u00e9 des sciences de l’UMMTO (Campus Hasnaoua II, dit aussi Bastos).<\/span><\/p>\n

Directeur de Th\u00e8se<\/strong>:<\/span> Pr. BEDOUHENE Fazia<\/strong><\/span><\/span>
Co-encadreur:<\/span> Pr Gasmi Farid<\/span><\/strong><\/span>
Devant le jury d’examen suivant :<\/span><\/p>\n\n\n\n\n\n\n\n\n
M.\u00a0 HAMAZ Abdelghani<\/b><\/td>\nProfesseur\u00a0<\/b><\/td>\nUMMTO<\/b><\/td>\nPr\u00e9sident<\/b><\/td>\n<\/tr>\n
Mme. BEDOUHENE Fazia<\/b><\/td>\nProfesseur<\/b><\/td>\nUMMTO<\/b><\/td>\nDirectrice de th\u00e8se\u00a0<\/b><\/td>\n<\/tr>\n
M.\u00a0 GASMI\u00a0 Farid<\/b><\/td>\nProfesseur<\/b><\/td>\nUTC, TSE, France<\/b><\/td>\nCo-encadreur<\/b><\/td>\n<\/tr>\n
M. ZOUGAB Nabil<\/b><\/td>\nProfesseur<\/b><\/td>\nU. Bejaia<\/b><\/td>\nExaminateur<\/b><\/td>\n<\/tr>\n
M. BOUALEM Mohammed<\/b><\/td>\nProfesseur<\/b><\/td>\nU. Bejaia<\/b><\/td>\nExaminateur<\/b><\/td>\n<\/tr>\n
Mme. ZIDELMAL Zahia<\/b><\/td>\nProfesseure<\/b><\/td>\nUMMTO<\/b><\/td>\nExaminatrice<\/b><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n

T\u00e9l\u00e9charger : Avis de soutenance ABDOUCHE Safia pdf<\/a><\/strong><\/p>\n

[\/et_pb_accordion_item][et_pb_accordion_item title=\u00a0\u00bbAbstract\u00a0\u00bb open_toggle_text_color=\u00a0\u00bb#0DA0C5″ _builder_version=\u00a0\u00bb4.27.4″ _module_preset=\u00a0\u00bbdefault\u00a0\u00bb hover_enabled=\u00a0\u00bb0″ box_shadow_style=\u00a0\u00bbpreset3″ box_shadow_color=\u00a0\u00bb#1FD7E0″ global_colors_info=\u00a0\u00bb{}\u00a0\u00bb toggle_text_color=\u00a0\u00bb#0DA0C5″ open=\u00a0\u00bboff\u00a0\u00bb sticky_enabled=\u00a0\u00bb0″]<\/p>\n

This thesis focuses on machine learning\u00a0 predictive models for regression. The objective is to reconcile theoretical rigor and empirical performance in the face of the growing challenges of prediction in real-world contexts marked by increasing data volume and complexity. After a presentation of the mathematical framework of machine learning and key concepts of statistical learning (real risk, empirical risk, bias\/variance trade-off, regularization, cross-validation, etc.), several algorithms are studied: linear models, nonlinear algorithms (decision trees, SVM, neural networks) and ensemble methods (bagging, boosting, stacking).<\/span><\/p>\n

A major contribution of this thesis lies in the development of two three-layer stacking architectures: a recursive architecture and an iterative one. These architectures have been rigorously evaluated on several real-world datasets, exploiting a combination of heterogeneous base models and optimized meta-models. The results obtained demonstrate superior performance compared to classical methods and even other ensemble methods such as random forests and the XGBoost algorithm, while offering increased flexibility to adapt to various contexts. This thesis also highlights the importance of base model diversity in ensemble approaches, a key criterion to guarantee the efficiency and robustness of the ensemble model. Ultimately, this work can be further developed by exploring even more complex architectures, integrating recent techniques from deep neural networks for the automatic optimization of stacking architectures.<\/span><\/p>\n

[\/et_pb_accordion_item][et_pb_accordion_item title=\u00a0\u00bbR\u00e9sum\u00e9\u00a0\u00bb open_toggle_text_color=\u00a0\u00bb#0DA0C5″ _builder_version=\u00a0\u00bb4.27.4″ _module_preset=\u00a0\u00bbdefault\u00a0\u00bb hover_enabled=\u00a0\u00bb0″ box_shadow_style=\u00a0\u00bbpreset3″ box_shadow_color=\u00a0\u00bb#1FD7E0″ global_colors_info=\u00a0\u00bb{}\u00a0\u00bb toggle_text_color=\u00a0\u00bb#0DA0C5″ open=\u00a0\u00bboff\u00a0\u00bb sticky_enabled=\u00a0\u00bb0″]<\/p>\n

Cette th\u00e8se porte sur les mod\u00e8les pr\u00e9dictifs de machine learning pour la r\u00e9gression. L\u2019objectif est de concilier rigueur th\u00e9orique et performance empirique face aux d\u00e9fis croissants de la pr\u00e9diction dans des contextes r\u00e9els marqu\u00e9s par l\u2019augmentation du volume et de la complexit\u00e9 des donn\u00e9es. Apr\u00e8s une pr\u00e9sentation du cadre math\u00e9matique du machine learning et des concepts cl\u00e9s de l’apprentissage statistique (risque r\u00e9el, risque empirique, compromis biais\/variance, r\u00e9gularisation, validation crois\u00e9e, etc.), plusieurs algorithmes sont \u00e9tudi\u00e9s : mod\u00e8les lin\u00e9aires, algorithmes non lin\u00e9aires (arbres de d\u00e9cision, SVM, r\u00e9seaux de neurones) et les m\u00e9thodes ensemblistes (bagging, boosting, stacking).<\/span><\/p>\n

Une contribution majeure de cette th\u00e8se r\u00e9side dans le d\u00e9veloppement de deux architectures de stacking \u00e0 trois couches : une architecture r\u00e9cursive et une autre it\u00e9rative. Ces architectures ont \u00e9t\u00e9 rigoureusement \u00e9valu\u00e9es sur plusieurs jeux de donn\u00e9es r\u00e9elles, en exploitant une combinaison de mod\u00e8les de base h\u00e9t\u00e9rog\u00e8nes et de m\u00e9ta-mod\u00e8les optimis\u00e9s. Les r\u00e9sultats obtenus d\u00e9montrent des performances sup\u00e9rieures \u00e0 celles des m\u00e9thodes classiques et m\u00eame aux autres m\u00e9thodes d\u2019ensemble comme les for\u00eats al\u00e9atoires et l\u2019algorithme XGBoost, tout en offrant une flexibilit\u00e9 accrue pour s\u2019adapter \u00e0 des contextes vari\u00e9s. Cette th\u00e8se souligne \u00e9galement l\u2019importance de la diversit\u00e9 des mod\u00e8les de base dans les approches ensemblistes, crit\u00e8re cl\u00e9 pour garantir l\u2019efficacit\u00e9 et la robustesse du mod\u00e8le global.\u00a0 Au final, ces travaux pourront \u00eatre approfondis en explorant des architectures encore plus complexes, en int\u00e9grant des techniques r\u00e9centes issues des r\u00e9seaux neuronaux profonds pour l\u2019optimisation automatique des architectures de stacking<\/span>.<\/span><\/p>\n

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Mme. ABDOUCHE SafiaSoutiendra publiquement sa th\u00e8se de Doctorat en Sciences en Math\u00e9matiquesIntitul\u00e9: << Les mod\u00e8les pr\u00e9dictifs de machine learning. >>Le: 12\/06\/2025 \u00e0 09h30, \u00e0 la salle de conf\u00e9rences de la facult\u00e9 des sciences de l’UMMTO (Campus Hasnaoua II, dit aussi Bastos). Directeur de Th\u00e8se: Pr. BEDOUHENE FaziaCo-encadreur: Pr Gasmi FaridDevant le jury d’examen suivant : […]<\/p>\n","protected":false},"author":13,"featured_media":132,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"categories":[1,13,9],"tags":[],"class_list":["post-28094","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-non-classe","category-actualites-pg","category-new_dep_maths"],"yoast_head":"\nAvis de soutenance de th\u00e8se de doctorat en-sciences math\u00e9matiques de Mme. 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