Готові списки джерел за темами / Machine learning interactif

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Добірка наукової літератури з теми "Machine learning interactif"

Автор: Grafiati
Опубліковано: 14 грудня 2024
Оновлено: 31 липня 2025

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Статті в журналах з теми "Machine learning interactif"

1

Chavel, Thierry. "La rencontre humaine est-elle soluble dans l’intelligence artificielle ?" Management international 28, no. 2 (2024): 142–44. http://dx.doi.org/10.59876/a-ma53-q5cw.

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Avec la numérisation du monde, la réalité n’est plus ce qu’elle était. Je peux avoir l’illusion d’être ici et ailleurs. Un cloud remplace ma mémoire personnelle. L’autre du débat s’efface au profit du même des communautés virtuelles. La 4e révolution industrielle n’est pas qu’un saut technologique, c’est surtout un choix de société qui renouvelle en profondeur l’exercice du leadership et ses trois fondements humanistes : la fragilité, l’altérité et la responsabilité. L’irruption d’outils de machine learning tels que Chat-GPT transforme violemment les métiers de la prestation intellectuelle. Un
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2

Li, Jiahang. "Research on Interactive System of Movie Subtitle Speech Based on Machine Learning Technology." Frontiers in Computing and Intelligent Systems 2, no. 2 (2022): 22–24. http://dx.doi.org/10.54097/fcis.v2i2.3744.

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Анотація:
The composition elements of subtitles, from the early single text, have developed into the present text, graphics, colors, animation, special effects and other combinations. With the development of speech technology and natural language understanding, speech interaction system has become a hot research field. Different from the traditional data interaction between keyboard, mouse and display, using hearing to transmit data makes the interactive system of movie subtitles more anthropomorphic and intelligent. It is the most natural and convenient means for human beings to exchange information wi
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3

Animesh, Kumar, and Dr Srikanth V. "Enhancing Healthcare through Human-Robot Interaction using AI and Machine Learning." International Journal of Research Publication and Reviews 5, no. 3 (2024): 184–90. http://dx.doi.org/10.55248/gengpi.5.0324.0831.

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An, Chang. "Student Status Supervision in Ideological and Political Machine Teaching Based on Machine Learning." E3S Web of Conferences 275 (2021): 03028. http://dx.doi.org/10.1051/e3sconf/202127503028.

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Анотація:
Under the premise of active in the field of machine learning, this paper takes online teaching system of ideological and Political education as an example to study machine learning and machine teaching system. In order to specifically understand the current situation of the construction and application of machine teaching based on supervised teaching of ideological and political theory courses in local colleges and universities, this experiment first conducted a statistical analysis of the learning results of the surveyed classes in two semesters from March 2020 to December 2020. The experimen
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Amershi, Saleema, James Fogarty, Ashish Kapoor, and Desney Tan. "Effective End-User Interaction with Machine Learning." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (2011): 1529–32. http://dx.doi.org/10.1609/aaai.v25i1.7964.

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Анотація:
End-user interactive machine learning is a promising tool for enhancing human productivity and capabilities with large unstructured data sets. Recent work has shown that we can create end-user interactive machine learning systems for specific applications. However, we still lack a generalized understanding of how to design effective end-user interaction with interactive machine learning systems. This work presents three explorations in designing for effective end-user interaction with machine learning in CueFlik, a system developed to support Web image search. These explorations demonstrate th
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Guo, Chao-Yu, and Ke-Hao Chang. "A Novel Algorithm to Estimate the Significance Level of a Feature Interaction Using the Extreme Gradient Boosting Machine." International Journal of Environmental Research and Public Health 19, no. 4 (2022): 2338. http://dx.doi.org/10.3390/ijerph19042338.

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Анотація:
Recent studies have revealed the importance of the interaction effect in cardiac research. An analysis would lead to an erroneous conclusion when the approach failed to tackle a significant interaction. Regression models deal with interaction by adding the product of the two interactive variables. Thus, statistical methods could evaluate the significance and contribution of the interaction term. However, machine learning strategies could not provide the p-value of specific feature interaction. Therefore, we propose a novel machine learning algorithm to assess the p-value of a feature interacti
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Zholshiyeva, Lazzat, Zhanat Manbetova, Dinara Kaibassova, et al. "Human-machine interactions based on hand gesture recognition using deep learning methods." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 1 (2024): 741–48. https://doi.org/10.11591/ijece.v14i1.pp741-748.

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Анотація:
Human interaction with computers and other machines is becoming an increasingly important and relevant topic in the modern world. Hand gesture recognition technology is an innovative approach to managing computers and electronic devices that allows users to interact with technology through gestures and hand movements. This article presents deep learning methods that allow you to efficiently process and classify hand gestures and hand gesture recognition technologies for interacting with computers. This paper discusses modern deep learning methods such as convolutional neural networks (CNN) and
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Spillard, Samuel, Christopher J. Turner, and Konstantinos Meichanetzidis. "Machine learning entanglement freedom." International Journal of Quantum Information 16, no. 08 (2018): 1840002. http://dx.doi.org/10.1142/s0219749918400026.

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Анотація:
Quantum many-body systems realize many different phases of matter characterized by their exotic emergent phenomena. While some simple versions of these properties can occur in systems of free fermions, their occurrence generally implies that the physics is dictated by an interacting Hamiltonian. The interaction distance has been successfully used to quantify the effect of interactions in a variety of states of matter via the entanglement spectrum [C. J. Turner, K. Meichanetzidis, Z. Papic and J. K. Pachos, Nat. Commun. 8 (2017) 14926, Phys. Rev. B 97 (2018) 125104]. The computation of the inte
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Kumar, Dr Tribhuwan, Klinge Orlando Villalba-Condori, Dennis Arias-Chavez, Rajesh K., Kalyan Chakravarthi M, and Dr Suman Rajest S. "An Evaluation on Speech Recognition Technology based on Machine Learning." Webology 19, no. 1 (2022): 646–63. http://dx.doi.org/10.14704/web/v19i1/web19046.

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Анотація:
Speech is the basic way of interaction between the listener to the speaker by voice or expression. Humans can easily understand the speakers' message, but machines can't understand the speaker's word. Nowadays, most of our lives are occupied by machines; but we can't interact with machines. The human brain, like machine learning technology, is essential for speech recognition to interact with machines to humans. The language used for speech recognition must be a global language, so English has been used in this paper. The machine learning methodology is used in a lot of assignments through the
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Coe, J. P. "Machine Learning Configuration Interaction." Journal of Chemical Theory and Computation 14, no. 11 (2018): 5739–49. http://dx.doi.org/10.1021/acs.jctc.8b00849.

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Дисертації з теми "Machine learning interactif"

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Scurto, Hugo. "Designing With Machine Learning for Interactive Music Dispositifs." Electronic Thesis or Diss., Sorbonne université, 2019. http://www.theses.fr/2019SORUS356.

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Анотація:
La musique est une pratique culturelle permettant aux êtres humains d'exprimer sensiblement leurs intentions à travers le son. L'apprentissage machine définit un ensemble de modèles permettant de nouvelles formes d'expression au sein desdits systèmes interactifs musicaux. Cependant, en tant que discipline informatique, l'apprentissage machine demeure essentiellement appliquée à la musique du point de vue des sciences de l'ingénieur, qui, très souvent, conçoit les modèles d'apprentissage sans tenir compte des interactions musicales prenant place entre humains et systèmes. Dans cette thèse, j'en
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Gosselin, Philippe-Henri. "Apprentissage interactif pour la recherche par le contenu dans les bases multimédias." Habilitation à diriger des recherches, Université de Cergy Pontoise, 2011. http://tel.archives-ouvertes.fr/tel-00660316.

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Анотація:
Les bases actuelles de données multimédia nécessitent des outils de plus en plus avancés pour pouvoir être parcourues avec efficacité. Dans ce contexte, la recherche en interaction avec un utilisateur est une approche qui permet de résoudre des requêtes à la sémantique complexe avec rapidité, sans pour autant nécessiter un haut niveau d'expertise utilisateur. Parmi les différents éléments intervenant dans la conception d'un système de recherche interactive, deux parties essentielles interviennent: l'indexation et la similarité entre les documents multimédia, et la gestion du processus interact
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Sungeelee, Vaynee. "Human-Machine Co-Learning : interactive curriculum generation for the acquisition of motor skills." Electronic Thesis or Diss., Sorbonne université, 2024. https://theses.hal.science/tel-04828514.

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Анотація:
L'acquisition de compétences motrices est le processus par lequel une personne est capable d'exécuter un mouvement avec plus de précision. Dans ce contexte, la pratique joue un rôle déterminant ; elle contribue à améliorer les performances de l'apprenant, mais n'y est pas toujours adapté. Un moyen de personnaliser l'apprentissage est de créer des séquences d'apprentissage qui conviennent à l'apprenant. Cependant, créer ces séquences manuellement demande du temps, ce qui rend cette démarche peu pratique. La génération automatique de séquences d'apprentissage peut remédier à ce problème. Les str
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Crochepierre, Laure. "Apprentissage automatique interactif pour les opérateurs du réseau électrique." Electronic Thesis or Diss., Université de Lorraine, 2022. http://www.theses.fr/2022LORR0112.

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Анотація:
Dans le contexte de la transition énergétique et de l'augmentation des interconnexions entre les réseaux de transport d'électricité en Europe, les opérateurs du réseau français doivent désormais faire face à davantage de fluctuations et des dynamiques nouvelles sur le réseau. Pour garantir la sûreté de ce réseau, les opérateurs s'appuient sur des logiciels informatiques permettant de réaliser des simulations, ou de suivre l'évolution d'indicateurs créés manuellement par des experts grâce à leur connaissance du fonctionnement du réseau. Le gestionnaire de réseau de transport d'électricité franç
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Lai, Hien Phuong. "Vers un système interactif de structuration des index pour une recherche par le contenu dans des grandes bases d'images." Phd thesis, Université de La Rochelle, 2013. http://tel.archives-ouvertes.fr/tel-00934842.

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Анотація:
Cette thèse s'inscrit dans la problématique de l'indexation et la recherche d'images par le contenu dans des bases d'images volumineuses. Les systèmes traditionnels de recherche d'images par le contenu se composent généralement de trois étapes: l'indexation, la structuration et la recherche. Dans le cadre de cette thèse, nous nous intéressons plus particulièrement à l'étape de structuration qui vise à organiser, dans une structure de données, les signatures visuelles des images extraites dans la phase d'indexation afin de faciliter, d'accélérer et d'améliorer les résultats de la recherche ulté
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Pace, Aaron J. "Guided Interactive Machine Learning." Diss., CLICK HERE for online access, 2006. http://contentdm.lib.byu.edu/ETD/image/etd1355.pdf.

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Krishna, Sooraj. "Modelling communicative behaviours for different roles of pedagogical agents." Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS286.

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Анотація:
Les agents dans un environnement d'apprentissage peuvent avoir divers rôles et comportements sociaux qui peuvent influencer les objectifs et la motivation des apprenants de différentes manières. L'apprentissage autorégulé (SRL) est un cadre conceptuel complet qui englobe les aspects cognitifs, métacognitifs, comportementaux, motivationnels et affectifs de l'apprentissage et implique les processus de définition d'objectifs, de suivi des progrès, d'analyse des commentaires, d'ajustement des objectifs et des actions de l'apprenant. Dans cette thèse, nous présentons une interaction d'apprentissage
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Schild, Erwan. "De l’importance de valoriser l’expertise humaine dans l’annotation : application à la modélisation de textes en intentions à l’aide d’un clustering interactif." Electronic Thesis or Diss., Université de Lorraine, 2024. http://www.theses.fr/2024LORR0024.

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Анотація:
La tâche d'annotation, nécessaire à l'entraînement d'assistants conversationnels, fait habituellement appel à des experts du domaine à modéliser. Toutefois, l'annotation de données est connue pour être une tâche difficile en raison de sa complexité et sa subjectivité : elle nécessite par conséquent de solides compétences analytiques dans le but de modéliser les textes en intention de dialogue. De ce fait, la plupart des projets d'annotation choisissent de former les experts aux tâches d'analyse pour en faire des "super-experts". Dans cette thèse, nous avons plutôt décidé mettre l'accent sur le
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Georgiev, Nikolay. "Assisting physiotherapists by designing a system utilising Interactive Machine Learning." Thesis, Uppsala universitet, Institutionen för informatik och media, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-447489.

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Анотація:
Millions of people throughout the world suffer from physical injuries and impairments and require physiotherapy to successfully recover. There are numerous obstacles in the way of having access to the necessary care – high costs, shortage of medical personnel and the need to travel to the appropriate medical facilities, something even more challenging during the Covid-19 pandemic. One approach to addressing this issue is to incorporate technology in the practice of physiotherapists, allowing them to help more patients. Using research through design, this thesis explores how interactive machine
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Kim, Been. "Interactive and interpretable machine learning models for human machine collaboration." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/98680.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2015.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 135-143).<br>I envision a system that enables successful collaborations between humans and machine learning models by harnessing the relative strength to accomplish what neither can do alone. Machine learning techniques and humans have skills that complement each other - machine learning techniques are good at computation on data at the lowest level of granularity, whereas people are better at abstracting
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Книги з теми "Machine learning interactif"

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Renals, Steve, Samy Bengio, and Jonathan G. Fiscus, eds. Machine Learning for Multimodal Interaction. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11965152.

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Popescu-Belis, Andrei, Steve Renals, and Hervé Bourlard, eds. Machine Learning for Multimodal Interaction. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78155-4.

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Bengio, Samy, and Hervé Bourlard, eds. Machine Learning for Multimodal Interaction. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/b105752.

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Popescu-Belis, Andrei, and Rainer Stiefelhagen, eds. Machine Learning for Multimodal Interaction. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-85853-9.

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Renals, Steve, and Samy Bengio, eds. Machine Learning for Multimodal Interaction. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11677482.

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Raedt, Luc de. Interactive theory revision: An inductive logic programming approach. Academic Press, 1992.

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Bösser, Tom. Learning in man-computer interaction: Areview of the literature. Springer-Verlag, 1987.

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Naidenova, Xenia. Machine learning methods for commonsense reasoning processes: Interactive models. Information Science Reference, 2010.

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Naidenova, Xenia. Machine learning methods for commonsense reasoning processes: Interactive models. Information Science Reference, 2010.

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Bösser, Tom. Learning in man-computer interaction: A review of the literature. Springer-Verlag, 1987.

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Частини книг з теми "Machine learning interactif"

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Wall, Emily, Soroush Ghorashi, and Gonzalo Ramos. "Using Expert Patterns in Assisted Interactive Machine Learning: A Study in Machine Teaching." In Human-Computer Interaction – INTERACT 2019. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29387-1_34.

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Drucker, Steven M., Danyel Fisher, and Sumit Basu. "Helping Users Sort Faster with Adaptive Machine Learning Recommendations." In Human-Computer Interaction – INTERACT 2011. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23765-2_13.

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Desolda, Giuseppe, Andrea Esposito, Rosa Lanzilotti, and Maria F. Costabile. "Detecting Emotions Through Machine Learning for Automatic UX Evaluation." In Human-Computer Interaction – INTERACT 2021. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85613-7_19.

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Lange, Marvin, Reuben Kirkham, and Benjamin Tannert. "Strategically Using Applied Machine Learning for Accessibility Documentation in the Built Environment." In Human-Computer Interaction – INTERACT 2021. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85616-8_25.

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Kühlwein, Daniel, Jasmin Christian Blanchette, Cezary Kaliszyk, and Josef Urban. "MaSh: Machine Learning for Sledgehammer." In Interactive Theorem Proving. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39634-2_6.

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Raber, Frederic, Felix Kosmalla, and Antonio Krueger. "Fine-Grained Privacy Setting Prediction Using a Privacy Attitude Questionnaire and Machine Learning." In Human-Computer Interaction – INTERACT 2017. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68059-0_48.

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Schrammel, Johann. "Exploring New Ways of Utilizing Automated Clustering and Machine Learning Techniques in Information Visualization." In Human-Computer Interaction – INTERACT 2011. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23768-3_41.

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Amunategui, Manuel, and Mehdi Roopaei. "Interactive Drawing Canvas and Digit Predictions Using TensorFlow on GCP." In Monetizing Machine Learning. Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3873-8_8.

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Kaiser, Michael, Volker Klingspor, and Holger Friedrich. "Human-Agent Interaction and Machine Learning." In Machine Learning: ECML-97. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-62858-4_98.

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Wang, Peng, and David B. Sawyer. "Translator–Computer Interaction Through Language Data." In Machine Learning in Translation. Routledge, 2023. http://dx.doi.org/10.4324/9781003321538-11.

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Тези доповідей конференцій з теми "Machine learning interactif"

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Katta, Jithendra, Nikhil Reddy Kodumuru, and Swathi Katta. "InterACT: Enhancing Human-AI Interaction." In 2025 International Conference on Machine Learning and Autonomous Systems (ICMLAS). IEEE, 2025. https://doi.org/10.1109/icmlas64557.2025.10967771.

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Lawrence, Tom, Tianyuan Zhang, Clarice Hilton, and Marco Gillies. "Interactive Machine Learning for Movement Interaction in VR." In 2025 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW). IEEE, 2025. https://doi.org/10.1109/vrw66409.2025.00081.

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Kopiler, Alberto, Tiago Novello, Guilherme Schardong, Luiz Schirmer, Daniel Perazzo, and Luiz Velho. "INTERACT-NET: An Interactive Interface for Multimedia Machine Learning." In 2024 37th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI). IEEE, 2024. http://dx.doi.org/10.1109/sibgrapi62404.2024.10716312.

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Wang, Weihao, Chyan Zheng Siow, Takenori Obo, and Naoyuki Kubota. "Long-Distance Gesture Recognition for Interactive Communication." In 2024 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2024. https://doi.org/10.1109/icmlc63072.2024.10935098.

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LIU, CHAO, XIZHAO WANG, and QIANG LIU. "Enhancing Drug-Drug Interaction Prediction by Knowledge Graph Embedding." In 2024 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2024. https://doi.org/10.1109/icmlc63072.2024.10935249.

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Gao, Yuan. "Emotion Recognition in Human-Robot Interaction: Multimodal Fusion, Deep Learning, and Ethical Considerations." In International Conference on Data Analysis and Machine Learning. SCITEPRESS - Science and Technology Publications, 2024. https://doi.org/10.5220/0013526100004619.

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Saranya, V. S., U. Ganesh Naidu, ParvathananiRajendra Kumar, E. Elamathi, JayavarapuKarthik, and AjithSundaram. "LookCursorAI: Machine Learning-Enhanced Eye-Powered Interaction." In 2024 IEEE 3rd World Conference on Applied Intelligence and Computing (AIC). IEEE, 2024. http://dx.doi.org/10.1109/aic61668.2024.10730891.

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Fails, Jerry Alan, and Dan R. Olsen. "Interactive machine learning." In the 8th international conference. ACM Press, 2003. http://dx.doi.org/10.1145/604045.604056.

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Plant, Nicola, Ruth Gibson, Carlos Gonzalez Diaz, et al. "Movement interaction design for immersive media using interactive machine learning." In MOCO '20: 7th International Conference on Movement and Computing. ACM, 2020. http://dx.doi.org/10.1145/3401956.3404252.

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Plant, Nicola, Clarice Hilton, Marco Gillies, et al. "Interactive Machine Learning for Embodied Interaction Design: A tool and methodology." In TEI '21: Fifteenth International Conference on Tangible, Embedded, and Embodied Interaction. ACM, 2021. http://dx.doi.org/10.1145/3430524.3442703.

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Звіти організацій з теми "Machine learning interactif"

1

Porter, Reid B., James P. Theiler, and Donald R. Hush. Interactive Machine Learning in Data Exploitation. Office of Scientific and Technical Information (OSTI), 2013. http://dx.doi.org/10.2172/1060903.

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2

Suter, Jonathan, Johnathan Cree, Jesse Johns, and Gianluca Longoni. Neural Interactive Machine Learning: Final Report: Compilation of presentation material. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1988291.

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3

Shukla, Indu, Rajeev Agrawal, Kelly Ervin, and Jonathan Boone. AI on digital twin of facility captured by reality scans. Engineer Research and Development Center (U.S.), 2023. http://dx.doi.org/10.21079/11681/47850.

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The power of artificial intelligence (AI) coupled with optimization algorithms can be linked to data-rich digital twin models to perform predictive analysis to make better informed decisions about installation operations and quality of life for the warfighters. In the current research, we developed AI connected lifecycle building information models through the creation of a data informed smart digital twin of one of US Army Corps of Engineers (USACE) buildings as our test case. Digital twin (DT) technology involves creating a virtual representation of a physical entity. Digital twin is created
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4

Pasupuleti, Murali Krishna. Quantum Cognition: Modeling Decision-Making with Quantum Theory. National Education Services, 2025. https://doi.org/10.62311/nesx/rrvi225.

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Abstract Quantum cognition applies quantum probability theory and mathematical principles from quantum mechanics to model human decision-making, reasoning, and cognitive processes beyond the constraints of classical probability models. Traditional decision theories, such as expected utility theory and Bayesian inference, struggle to explain context-dependent reasoning, preference reversals, order effects, and cognitive biases observed in human behavior. By incorporating superposition, interference, and entanglement, quantum cognitive models offer a probabilistic framework that better accounts
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5

Wilson, D., Steven Peckham, Max Krackow, Sora Haley, Sophia Bragdon, and Jay Clausen. Discriminating buried munitions based on physical models for their thermal response. Engineer Research and Development Center (U.S.), 2025. https://doi.org/10.21079/11681/49749.

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Munitions and other objects buried near the Earth’s surface can often be recognized in infrared imagery because their thermal and radiative properties differ from the surrounding undisturbed soil. However, the evolution of the thermal signature over time is subject to many complex interacting processes, including incident solar radiation, heat conduction in the ground, longwave radiation from the surface, and sensible and latent heat exchanges with the atmosphere. This complexity makes development of robust classification algorithms particularly challenging. Machine-learning algorithms, althou
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6

Bao, Jieyi, Xiaoqiang Hu, Cheng Peng, et al. Advancing INDOT’s Friction Test Program for Seamless Coverage of System: Pavement Markings, Typical Aggregates, Color Surface Treatment, and Horizontal Curves. Purdue University, 2024. http://dx.doi.org/10.5703/1288284317734.

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Various highway projects, roadway safety, and maintenance all hinge on pavement friction. INDOT's pavement friction test program has played a crucial role in addressing issues like wet pavement crash reduction, durable pavements surface friction, and sustainable aggregates. However, changes in the transportation sector, allied industries, societal needs, and economics present unique challenges that require proactive solutions. First, the existing field friction testing method, which uses a locked wheel skid tester (LWST) is limited to straight, flat pavement sections and excludes crash-prone a
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