Literatura académica sobre el tema "Metric learning paradigm"
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Artículos de revistas sobre el tema "Metric learning paradigm"
Brockmeier, Austin J., John S. Choi, Evan G. Kriminger, Joseph T. Francis y Jose C. Principe. "Neural Decoding with Kernel-Based Metric Learning". Neural Computation 26, n.º 6 (junio de 2014): 1080–107. http://dx.doi.org/10.1162/neco_a_00591.
Texto completoSaha, Soumadeep, Utpal Garain, Arijit Ukil, Arpan Pal y Sundeep Khandelwal. "MedTric : A clinically applicable metric for evaluation of multi-label computational diagnostic systems". PLOS ONE 18, n.º 8 (10 de agosto de 2023): e0283895. http://dx.doi.org/10.1371/journal.pone.0283895.
Texto completoGong, Xiuwen, Dong Yuan y Wei Bao. "Online Metric Learning for Multi-Label Classification". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 04 (3 de abril de 2020): 4012–19. http://dx.doi.org/10.1609/aaai.v34i04.5818.
Texto completoQiu, Wei. "Based on Semi-Supervised Clustering with the Boost Similarity Metric Method for Face Retrieval". Applied Mechanics and Materials 543-547 (marzo de 2014): 2720–23. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.2720.
Texto completoXiao, Qiao, Khuan Lee, Siti Aisah Mokhtar, Iskasymar Ismail, Ahmad Luqman bin Md Pauzi, Qiuxia Zhang y Poh Ying Lim. "Deep Learning-Based ECG Arrhythmia Classification: A Systematic Review". Applied Sciences 13, n.º 8 (14 de abril de 2023): 4964. http://dx.doi.org/10.3390/app13084964.
Texto completoNiu, Gang, Bo Dai, Makoto Yamada y Masashi Sugiyama. "Information-Theoretic Semi-Supervised Metric Learning via Entropy Regularization". Neural Computation 26, n.º 8 (agosto de 2014): 1717–62. http://dx.doi.org/10.1162/neco_a_00614.
Texto completoWilde, Henry, Vincent Knight y Jonathan Gillard. "Evolutionary dataset optimisation: learning algorithm quality through evolution". Applied Intelligence 50, n.º 4 (27 de diciembre de 2019): 1172–91. http://dx.doi.org/10.1007/s10489-019-01592-4.
Texto completoZhukov, Alexey, Jenny Benois-Pineau y Romain Giot. "Evaluation of Explanation Methods of AI - CNNs in Image Classification Tasks with Reference-based and No-reference Metrics". Advances in Artificial Intelligence and Machine Learning 03, n.º 01 (2023): 620–46. http://dx.doi.org/10.54364/aaiml.2023.1143.
Texto completoPinto, Danna, Anat Prior y Elana Zion Golumbic. "Assessing the Sensitivity of EEG-Based Frequency-Tagging as a Metric for Statistical Learning". Neurobiology of Language 3, n.º 2 (2022): 214–34. http://dx.doi.org/10.1162/nol_a_00061.
Texto completoGomoluch, Paweł, Dalal Alrajeh y Alessandra Russo. "Learning Classical Planning Strategies with Policy Gradient". Proceedings of the International Conference on Automated Planning and Scheduling 29 (25 de mayo de 2021): 637–45. http://dx.doi.org/10.1609/icaps.v29i1.3531.
Texto completoTesis sobre el tema "Metric learning paradigm"
Berry, Chadwick Alan. "The fidelity of long-term memory for perceptual magnitudes, symbolic vs. metric learning paradigms". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ29183.pdf.
Texto completoLibros sobre el tema "Metric learning paradigm"
The fidelity of long-term memory for perceptual magnitudes: Symbolic vs. metric learning paradigms. Ottawa: National Library of Canada = Bibliothèque nationale du Canada, 1999.
Buscar texto completoCapítulos de libros sobre el tema "Metric learning paradigm"
Biehl, Michael, Barbara Hammer, Petra Schneider y Thomas Villmann. "Metric Learning for Prototype-Based Classification". En Innovations in Neural Information Paradigms and Applications, 183–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04003-0_8.
Texto completoStevens, Alexander, Johannes De Smedt y Jari Peeperkorn. "Quantifying Explainability in Outcome-Oriented Predictive Process Monitoring". En Lecture Notes in Business Information Processing, 194–206. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98581-3_15.
Texto completoBarbalet, Thomas S. "Noble Ape’s Cognitive Simulation". En Machine Learning, 1839–55. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-60960-818-7.ch709.
Texto completoAnand, Poonam y Starr Ackley. "Equitable Assessment and Evaluation of Young Language Learners". En Advances in Early Childhood and K-12 Education, 84–107. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-6487-5.ch005.
Texto completoMarkowitz, John C. "Interpersonal Psychotherapy". En In the Aftermath of the Pandemic, 18–39. Oxford University Press, 2021. http://dx.doi.org/10.1093/med-psych/9780197554500.003.0004.
Texto completoCatal, Cagatay y Soumya Banerjee. "Application of Artificial Immune Systems Paradigm for Developing Software Fault Prediction Models". En Machine Learning, 371–87. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-60960-818-7.ch302.
Texto completoIvanov, Bogdan, Victorița Trif y Ana Trif. "Assessment and Paradigms". En Analyzing Paradigms Used in Education and Educational Psychology, 121–43. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1427-6.ch006.
Texto completoMadan, Shipra, Tapan Kumar Gandhi y Santanu Chaudhury. "Bone age assessment using metric learning on small dataset of hand radiographs". En Advanced Machine Vision Paradigms for Medical Image Analysis, 259–71. Elsevier, 2021. http://dx.doi.org/10.1016/b978-0-12-819295-5.00010-x.
Texto completoSuganthi, J., B. Nagarajan y S. Muhtumari. "Network Anomaly Detection Using Hybrid Deep Learning Technique". En Advances in Parallel Computing Algorithms, Tools and Paradigms. IOS Press, 2022. http://dx.doi.org/10.3233/apc220014.
Texto completoAdriaans, Pieter. "A Computational Theory of Meaning". En Advances in Info-Metrics, 32–78. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780190636685.003.0002.
Texto completoActas de conferencias sobre el tema "Metric learning paradigm"
Gao, Qiang, Xiaohan Wang, Chaoran Liu, Goce Trajcevski, Li Huang y Fan Zhou. "Open Anomalous Trajectory Recognition via Probabilistic Metric Learning". En Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/233.
Texto completoYonghe, Chu, Hongfei Lin, Liang Yang, Yufeng Diao, Shaowu Zhang y Fan Xiaochao. "Refining Word Representations by Manifold Learning". En Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/749.
Texto completoXue, Wanqi, Youzhi Zhang, Shuxin Li, Xinrun Wang, Bo An y Chai Kiat Yeo. "Solving Large-Scale Extensive-Form Network Security Games via Neural Fictitious Self-Play". En Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/511.
Texto completoHayes, Tyler L., Ronald Kemker, Nathan D. Cahill y Christopher Kanan. "New Metrics and Experimental Paradigms for Continual Learning". En 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2018. http://dx.doi.org/10.1109/cvprw.2018.00273.
Texto completoAsedegbega, Jerome, Oladayo Ayinde y Alexander Nwakanma. "Application of Machine Learniing For Reservoir Facies Classification in Port Field, Offshore Niger Delta". En SPE Nigeria Annual International Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/207163-ms.
Texto completoPribeanu, Costin y Vincentas Lamanauskas. "USEFULNESS OF FACEBOOK FOR STUDENTS: ANALYSIS OF UNIVERSITY PROFILE DIFFERENCES FROM A MULTIDIMENSIONAL PERSPECTIVE". En eLSE 2016. Carol I National Defence University Publishing House, 2016. http://dx.doi.org/10.12753/2066-026x-16-170.
Texto completoDos Santos, Fernando Pereira y Moacir Antonelli Ponti. "Features transfer learning for image and video recognition tasks". En Conference on Graphics, Patterns and Images. Sociedade Brasileira de Computação, 2020. http://dx.doi.org/10.5753/sibgrapi.est.2020.12980.
Texto completoAlbeanu, Grigore y Marin Vlada. "NEUTROSOPHIC APPROACHES IN E-LEARNING ASSESSMENT". En eLSE 2014. Editura Universitatii Nationale de Aparare "Carol I", 2014. http://dx.doi.org/10.12753/2066-026x-14-208.
Texto completoGimenez, Paulo Jose de Alcantara, Marcelo De Oliveira Costa Machado, Cleber Pinelli Pinelli y Sean Wolfgand Matsui Siqueira. "Investigating the learning perspective of Searching as Learning, a review of the state of the art". En Simpósio Brasileiro de Informática na Educação. Sociedade Brasileira de Computação, 2020. http://dx.doi.org/10.5753/cbie.sbie.2020.302.
Texto completoZheng, Meng, Srikrishna Karanam, Terrence Chen, Richard J. Radke y Ziyan Wu. "Visual Similarity Attention". En Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/241.
Texto completoInformes sobre el tema "Metric learning paradigm"
Perdigão, Rui A. P. Information physics and quantum space technologies for natural hazard sensing, modelling and prediction. Meteoceanics, septiembre de 2021. http://dx.doi.org/10.46337/210930.
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