Artículos de revistas sobre el tema "Metric learning paradigm"
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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 completoDou, Jason Xiaotian, Lei Luo y Raymond Mingrui Yang. "An Optimal Transport Approach to Deep Metric Learning (Student Abstract)". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 11 (28 de junio de 2022): 12935–36. http://dx.doi.org/10.1609/aaai.v36i11.21604.
Texto completoWang, Yabin, Zhiheng Ma, Zhiwu Huang, Yaowei Wang, Zhou Su y Xiaopeng Hong. "Isolation and Impartial Aggregation: A Paradigm of Incremental Learning without Interference". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 8 (26 de junio de 2023): 10209–17. http://dx.doi.org/10.1609/aaai.v37i8.26216.
Texto completoGe, Ce, Jingyu Wang, Qi Qi, Haifeng Sun, Tong Xu y Jianxin Liao. "Semi-transductive Learning for Generalized Zero-Shot Sketch-Based Image Retrieval". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 6 (26 de junio de 2023): 7678–86. http://dx.doi.org/10.1609/aaai.v37i6.25931.
Texto completoDe Santis, Enrico, Alessio Martino y Antonello Rizzi. "On component-wise dissimilarity measures and metric properties in pattern recognition". PeerJ Computer Science 8 (10 de octubre de 2022): e1106. http://dx.doi.org/10.7717/peerj-cs.1106.
Texto completoJaiswal, Mimansa y Emily Mower Provost. "Privacy Enhanced Multimodal Neural Representations for Emotion Recognition". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 05 (3 de abril de 2020): 7985–93. http://dx.doi.org/10.1609/aaai.v34i05.6307.
Texto completoYuan, Fei, Longtu Zhang, Huang Bojun y Yaobo Liang. "Simpson's Bias in NLP Training". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 16 (18 de mayo de 2021): 14276–83. http://dx.doi.org/10.1609/aaai.v35i16.17679.
Texto completoKhan, Koffka y Wayne Goodridge. "Comparative study of One-Shot Learning in Dynamic Adaptive Streaming over HTTP : A Taxonomy-Based Analysis". International Journal of Advanced Networking and Applications 15, n.º 01 (2023): 5822–30. http://dx.doi.org/10.35444/ijana.2023.15112.
Texto completoWOLFMAN, STEVEN A. y DANIEL S. WELD. "Combining linear programming and satisfiability solving for resource planning". Knowledge Engineering Review 16, n.º 1 (marzo de 2001): 85–99. http://dx.doi.org/10.1017/s0269888901000017.
Texto completoLin, Jianman, Jiantao Lin, Yuefang Gao, Zhijing Yang y Tianshui Chen. "Webly Supervised Fine-Grained Image Recognition with Graph Representation and Metric Learning". Electronics 11, n.º 24 (11 de diciembre de 2022): 4127. http://dx.doi.org/10.3390/electronics11244127.
Texto completoSamann, Fady Esmat Fathel, Adnan Mohsin Abdulazeez y Shavan Askar. "Fog Computing Based on Machine Learning: A Review". International Journal of Interactive Mobile Technologies (iJIM) 15, n.º 12 (18 de junio de 2021): 21. http://dx.doi.org/10.3991/ijim.v15i12.21313.
Texto completoElfakharany, Ahmed y Zool Hilmi Ismail. "End-to-End Deep Reinforcement Learning for Decentralized Task Allocation and Navigation for a Multi-Robot System". Applied Sciences 11, n.º 7 (24 de marzo de 2021): 2895. http://dx.doi.org/10.3390/app11072895.
Texto completoSotiropoulos, Dionisios N., Efthimios Alepis, Katerina Kabassi, Maria K. Virvou, George A. Tsihrintzis y Evangelos Sakkopoulos. "Artificial Immune System-Based Learning Style Stereotypes". International Journal on Artificial Intelligence Tools 28, n.º 04 (junio de 2019): 1940008. http://dx.doi.org/10.1142/s0218213019400086.
Texto completoMwata-Velu, Tat’y, Juan Gabriel Avina-Cervantes, Jose Ruiz-Pinales, Tomas Alberto Garcia-Calva, Erick-Alejandro González-Barbosa, Juan B. Hurtado-Ramos y José-Joel González-Barbosa. "Improving Motor Imagery EEG Classification Based on Channel Selection Using a Deep Learning Architecture". Mathematics 10, n.º 13 (1 de julio de 2022): 2302. http://dx.doi.org/10.3390/math10132302.
Texto completoLiu, Pingping, Guixia Gou, Xue Shan, Dan Tao y Qiuzhan Zhou. "Global Optimal Structured Embedding Learning for Remote Sensing Image Retrieval". Sensors 20, n.º 1 (4 de enero de 2020): 291. http://dx.doi.org/10.3390/s20010291.
Texto completoLi, Hui, Jinhao Liu y Dian Wang. "A Fast Instance Segmentation Technique for Log End Faces Based on Metric Learning". Forests 14, n.º 4 (13 de abril de 2023): 795. http://dx.doi.org/10.3390/f14040795.
Texto completoMotaung, William B., Kingsley A. Ogudo y Chabalala S. Chabalala. "Optimal Video Compression Parameter Tuning for Digital Video Broadcasting (DVB) using Deep Reinforcement Learning". International Conference on Intelligent and Innovative Computing Applications 2022 (31 de diciembre de 2022): 270–76. http://dx.doi.org/10.59200/iconic.2022.030.
Texto completoBenvenuto, Giovana A., Marilaine Colnago, Maurício A. Dias, Rogério G. Negri, Erivaldo A. Silva y Wallace Casaca. "A Fully Unsupervised Deep Learning Framework for Non-Rigid Fundus Image Registration". Bioengineering 9, n.º 8 (5 de agosto de 2022): 369. http://dx.doi.org/10.3390/bioengineering9080369.
Texto completoSakakushev, Boris E., Blagoi I. Marinov, Penka P. Stefanova, Stefan St Kostianev y Evangelos K. Georgiou. "Striving for Better Medical Education: the Simulation Approach". Folia Medica 59, n.º 2 (1 de junio de 2017): 123–31. http://dx.doi.org/10.1515/folmed-2017-0039.
Texto completoKuang, Jiachen, Tangfei Tao, Qingqiang Wu, Chengcheng Han, Fan Wei, Shengchao Chen, Wenjie Zhou, Cong Yan y Guanghua Xu. "Domain-Adaptive Prototype-Recalibrated Network with Transductive Learning Paradigm for Intelligent Fault Diagnosis under Various Limited Data Conditions". Sensors 22, n.º 17 (30 de agosto de 2022): 6535. http://dx.doi.org/10.3390/s22176535.
Texto completoManzoor, Sumaira, Ye-Chan An, Gun-Gyo In, Yueyuan Zhang, Sangmin Kim y Tae-Yong Kuc. "SPT: Single Pedestrian Tracking Framework with Re-Identification-Based Learning Using the Siamese Model". Sensors 23, n.º 10 (19 de mayo de 2023): 4906. http://dx.doi.org/10.3390/s23104906.
Texto completoXu, Yanbing, Yanmei Zhang, Tingxuan Yue, Chengcheng Yu y Huan Li. "Graph-Based Domain Adaptation Few-Shot Learning for Hyperspectral Image Classification". Remote Sensing 15, n.º 4 (18 de febrero de 2023): 1125. http://dx.doi.org/10.3390/rs15041125.
Texto completoAlshammari, Abdulaziz y Rakan C. Chabaan. "Sppn-Rn101: Spatial Pyramid Pooling Network with Resnet101-Based Foreign Object Debris Detection in Airports". Mathematics 11, n.º 4 (7 de febrero de 2023): 841. http://dx.doi.org/10.3390/math11040841.
Texto completoLi, Shuyuan, Huabin Liu, Rui Qian, Yuxi Li, John See, Mengjuan Fei, Xiaoyuan Yu y Weiyao Lin. "TA2N: Two-Stage Action Alignment Network for Few-Shot Action Recognition". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 2 (28 de junio de 2022): 1404–11. http://dx.doi.org/10.1609/aaai.v36i2.20029.
Texto completoAlonso-Betanzos, Amparo, Verónica Bolón-Canedo, Guy R. Heyndrickx y Peter L. M. Kerkhof. "Exploring Guidelines for Classification of Major Heart Failure Subtypes by Using Machine Learning". Clinical Medicine Insights: Cardiology 9s1 (enero de 2015): CMC.S18746. http://dx.doi.org/10.4137/cmc.s18746.
Texto completoUzair, Muhammad, Mohsen Eskandari, Li Li y Jianguo Zhu. "Machine Learning Based Protection Scheme for Low Voltage AC Microgrids". Energies 15, n.º 24 (12 de diciembre de 2022): 9397. http://dx.doi.org/10.3390/en15249397.
Texto completoMartinelli, M., C. J. A. P. Martins, S. Nesseris, D. Sapone, I. Tutusaus, A. Avgoustidis, S. Camera et al. "Euclid: Forecast constraints on the cosmic distance duality relation with complementary external probes". Astronomy & Astrophysics 644 (diciembre de 2020): A80. http://dx.doi.org/10.1051/0004-6361/202039078.
Texto completoLyu, Yangxintong, Ionut Schiopu, Bruno Cornelis y Adrian Munteanu. "Framework for Vehicle Make and Model Recognition—A New Large-Scale Dataset and an Efficient Two-Branch–Two-Stage Deep Learning Architecture". Sensors 22, n.º 21 (2 de noviembre de 2022): 8439. http://dx.doi.org/10.3390/s22218439.
Texto completoAmari, Shun-ichi, Hyeyoung Park y Tomoko Ozeki. "Singularities Affect Dynamics of Learning in Neuromanifolds". Neural Computation 18, n.º 5 (mayo de 2006): 1007–65. http://dx.doi.org/10.1162/neco.2006.18.5.1007.
Texto completoVoulodimos, Athanasios, Eftychios Protopapadakis, Iason Katsamenis, Anastasios Doulamis y Nikolaos Doulamis. "A Few-Shot U-Net Deep Learning Model for COVID-19 Infected Area Segmentation in CT Images". Sensors 21, n.º 6 (22 de marzo de 2021): 2215. http://dx.doi.org/10.3390/s21062215.
Texto completoAnand, S. S., P. W. Hamilton, J. G. Hughes y D. A. Bell. "On Prognostic Models, Artificial Intelligence and Censored Observations". Methods of Information in Medicine 40, n.º 01 (2001): 18–24. http://dx.doi.org/10.1055/s-0038-1634459.
Texto completoYAN, YUHONG y HAN LIANG. "LAZY LEARNER ON DECISION TREE FOR RANKING". International Journal on Artificial Intelligence Tools 17, n.º 01 (febrero de 2008): 139–58. http://dx.doi.org/10.1142/s0218213008003819.
Texto completoRayala, Venkat y Satyanarayan Reddy Kalli. "Big Data Clustering Using Improvised Fuzzy C-Means Clustering". Revue d'Intelligence Artificielle 34, n.º 6 (31 de diciembre de 2020): 701–8. http://dx.doi.org/10.18280/ria.340604.
Texto completoLongo, Mathias, Matías Hirsch, Cristian Mateos y Alejandro Zunino. "Towards Integrating Mobile Devices into Dew Computing: A Model for Hour-Wise Prediction of Energy Availability". Information 10, n.º 3 (26 de febrero de 2019): 86. http://dx.doi.org/10.3390/info10030086.
Texto completoTamm, Markus-Oliver, Yar Muhammad y Naveed Muhammad. "Classification of Vowels from Imagined Speech with Convolutional Neural Networks". Computers 9, n.º 2 (1 de junio de 2020): 46. http://dx.doi.org/10.3390/computers9020046.
Texto completoDave, Chitrak Vimalbhai. "An Efficient Framework for Cost and Effort Estimation of Scrum Projects". International Journal for Research in Applied Science and Engineering Technology 9, n.º 11 (30 de noviembre de 2021): 1478–87. http://dx.doi.org/10.22214/ijraset.2021.39030.
Texto completoGalindo-Noreña, Steven, David Cárdenas-Peña y Álvaro Orozco-Gutierrez. "Multiple Kernel Stein Spatial Patterns for the Multiclass Discrimination of Motor Imagery Tasks". Applied Sciences 10, n.º 23 (2 de diciembre de 2020): 8628. http://dx.doi.org/10.3390/app10238628.
Texto completoFeng, Jialiang y Jie Gong. "AoI-Aware Optimization of Service Caching-Assisted Offloading and Resource Allocation in Edge Cellular Networks". Sensors 23, n.º 6 (21 de marzo de 2023): 3306. http://dx.doi.org/10.3390/s23063306.
Texto completoPowers, David. "Unsupervised Learning of Linguistic Structure". International Journal of Corpus Linguistics 2, n.º 1 (1 de enero de 1997): 91–131. http://dx.doi.org/10.1075/ijcl.2.1.06pow.
Texto completoAzam, Abu Bakr, Yu Qing Chang, Matthew Leong Tze Ker, Denise Goh, Jeffrey Chun Tatt Lim, Mai Chan Lau, Benedict Tan, Lihui Huang, Joe Yeong y Yiyu Cai. "818 Using deep learning approaches with mIF images to enhance T cell identification for tumor -automation of infiltrating lymphocytes (TILs) scoring on H&E images". Journal for ImmunoTherapy of Cancer 9, Suppl 2 (noviembre de 2021): A855—A856. http://dx.doi.org/10.1136/jitc-2021-sitc2021.818.
Texto completoSamtani, Sagar, Yidong Chai y Hsinchun Chen. "Linking Exploits from the Dark Web to Known Vulnerabilities for Proactive Cyber Threat Intelligence: An Attention-Based Deep Structured Semantic Model". MIS Quarterly 46, n.º 2 (24 de mayo de 2022): 911–46. http://dx.doi.org/10.25300/misq/2022/15392.
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