Semi-supervised Learning Based on Graph Stochastic Co-Training
Pourquoi ce travail est dans la base
Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.
Notice bibliographique
Résumé
[1]\tR. E. Bellman, Dynamic programming. Princeton: Princeton University Press, 1957. p. ix ISBN 978-0-691-07951-6. \n[2]\tA. Blum and T. Mitchell, “Combining labeled and unlabeled data with co-training,” COLT' 98: Proceedings of the eleventh annual conference on Computational learning theory, July 1998, pp. 92–100, Madison, Wisconsin, United States, 24–26 July 1998, New York, New York, USA, https://doi.org/10.1145/279943.279962 \n[3]\tOlivier Chapelle, Bernhard Schölkopf, and Alexander Zien, "Semi-supervised learning," MIT Press, 2006, pp. 193–205, ISBN:978-0-262-03358-9. \n[4]\tJ. Chan, I. Koprinska and J. Poon, “Co-training with a Single Natural Feature Set Applied to Email Classification,” In proceeding Conference on Web Intelligence, Beijing, China, 2004. \n[5]\tK. Nigam and R. Ghani, “Analyzing the Effectiveness and Applicability of Co-Training,” In Proceeding of the 9th, International Conference on Information and Knowledge Management, McLean, Virginia, USA, 2000. https://doi.org/10.1145/354756.354805 \n[6]\tMinmin Chen & Kilian Weinberger, “Automatic Feature Decomposition for Single View Co-training,” Proceedings of the 28th International Conference on Machine Learning, ICML 2011. 953–960. \n[7]\tW. Zhang and Q. Zheng, "TSFS: A Novel Algorithm for Single View Co-training," 2009 International Joint Conference on Computational Sciences and Optimization, Sanya, China, 2009, pp. 492–496, https://doi: 10.1109/CSO.2009.251. \n[8]\tU. N. Raghavan, R. Albert, S. Kumara, “Near linear time algorithm to detect community structures in large-scale networks,” Phys. Rev. E Stat. Nonlinear Soft Matter Phys. Rev., E76, 036106, 2007. https://doi.org/10.1103/PhysRevE.76.036106 \n[9]\tX. Liu, T. Murata, “Advanced modularity-specialized label propagation algorithm for detecting communities in networks,” Phys. A: Stat. Mech. and Appl., vol. 389, pp. 1493–1500, 2012. https://doi.org/10.1016/j.physa.2009.12.019 \n[10]\tJ. Xie and B. K. Szymanski, “Community Detection Using a Neighborhood Strength Driven Label Propagation Algorithm,” In Proceedings of the 2011 IEEE Network Science Workshop, IEEE Computer Society, West Point, NY, USA, 22–24 June 2011, pp. 188–195. https://doi.org/10.1109/NSW.2011.6004645 \n[11]\tG. Cordasco and L. Gargano, “Community detection via semi-synchronous label propagation algorithms,” In Proceedings of the IEEE International Workshop on Business Applications of Social Network Analysis, Bangalore, India, 15 December 2011, pp. 1–8. https://doi.org/10.1109/BASNA.2010.5730298 \n[12]\tChun Gui, Ruisheng Zhang, Zhili Zhao, Jiaxuan Wei, and Rongjing Hu, “LPA-CBD An Improved Label Propagation Algorithm Based on Community Belonging Degree for Community Detection,” Int. J. Mod. Phys. C, vol. 29, no. 02, 1850011, 2018. https://doi.org/10.1142/S0129183118500110 \n[13]\tYan Xing, Fanrong Meng, Yong Zhou, Mu Zhu, Mengyu Shi, and Guibin Sun, "A Node Influence Based Label Propagation Algorithm for Community Detection in Networks", The Scientific World Journal, vol. 2014, Article ID 627581, 13 p., 2014. https://doi.org/10.1155/2014/627581 \n[14]\tX. K. Zhang, J. Ren, C. Song, J. Jia, and Q. Zhang, “Label propagation algorithm for community detection based on node importance and label influence,” Phys. Lett. A, vol. 381, Issue 33, pp. 2691–2698, 2017, https://doi.org/10.1016/j.physleta.2017.06.018 \n[15]\tHuan Li, Ruisheng Zhang, Zhili Zhao, and Xin Liu, “LPA-MNI: An Improved Label Propagation Algorithm Based on Modularity and Node Importance for Community Detection,” Entropy, 23(5), 497. https://doi.org/10.3390/e23050497. \n[16]\tS. Gregory, “Finding overlapping communities in networks by label propagation,” New J. Phys., vol. 12, pp. 2011–2024, 2010, https://doi.org/10.1088/1367-2630/12/10/103018 \n[17]\tJ. Xie, B. K. Szymanski, and X. Liu, “SLPA: Uncovering Overlapping Communities in Social Networks via a Speaker-Listener Interaction Dynamic Process,” In Proceedings of the IEEE International Conference on Data Mining Workshops, Vancouver, BC, Canada, 11 December 2012, pp. 344–349. https://doi.org/10.1109/ICDMW.2011.154 \n[18]\tZ. Song, X. Yang, Z. Xu and I. King, "Graph-Based Semi-Supervised Learning: A Comprehensive Review," in IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 11, pp. 8174–8194, Nov. 2023, https://doi.org/10.1109/TNNLS.2022.3155478. \n[19]\tDe-Ming Liang & Yu-Feng Li, “Lightweight Label Propagation for Large-Scale Network Data,” Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence Main track, 2018, pp. 3421–3427. https://doi.org/10.24963/ijcai.2018/475
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle