Добірка наукової літератури з теми "Data and human knowledge learning"
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Ознайомтеся зі списками актуальних статей, книг, дисертацій, тез та інших наукових джерел на тему "Data and human knowledge learning".
Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.
Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.
Статті в журналах з теми "Data and human knowledge learning"
Dudyrev, Egor, Ilia Semenkov, Sergei O. Kuznetsov, Gleb Gusev, Andrew Sharp, and Oleg S. Pianykh. "Human knowledge models: Learning applied knowledge from the data." PLOS ONE 17, no. 10 (October 20, 2022): e0275814. http://dx.doi.org/10.1371/journal.pone.0275814.
Повний текст джерелаWeber, Patrick, Nicolas Weber, Michael Goesele, and Rüdiger Kabst. "Prospect for Knowledge in Survey Data." Social Science Computer Review 36, no. 5 (September 12, 2017): 575–90. http://dx.doi.org/10.1177/0894439317725836.
Повний текст джерелаYao, Quanming. "Towards Human-like Learning from Relational Structured Data." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 20 (March 24, 2024): 22684. http://dx.doi.org/10.1609/aaai.v38i20.30300.
Повний текст джерелаKulikovskikh, Ilona, Tomislav Lipic, and Tomislav Šmuc. "From Knowledge Transmission to Knowledge Construction: A Step towards Human-Like Active Learning." Entropy 22, no. 8 (August 18, 2020): 906. http://dx.doi.org/10.3390/e22080906.
Повний текст джерелаAnderson, John R. "Methodologies for studying human knowledge." Behavioral and Brain Sciences 10, no. 3 (September 1987): 467–77. http://dx.doi.org/10.1017/s0140525x00023554.
Повний текст джерелаKwak, Beong-woo, Youngwook Kim, Yu Jin Kim, Seung-won Hwang, and Jinyoung Yeo. "TrustAL: Trustworthy Active Learning Using Knowledge Distillation." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 7 (June 28, 2022): 7263–71. http://dx.doi.org/10.1609/aaai.v36i7.20688.
Повний текст джерелаAngrist, Noam, Simeon Djankov, Pinelopi K. Goldberg, and Harry A. Patrinos. "Measuring human capital using global learning data." Nature 592, no. 7854 (March 10, 2021): 403–8. http://dx.doi.org/10.1038/s41586-021-03323-7.
Повний текст джерелаKalaycı, Tahir Emre, Bor Bricelj, Marko Lah, Franz Pichler, Matthias K. Scharrer, and Jelena Rubeša-Zrim. "A Knowledge Graph-Based Data Integration Framework Applied to Battery Data Management." Sustainability 13, no. 3 (February 2, 2021): 1583. http://dx.doi.org/10.3390/su13031583.
Повний текст джерелаSinger-Brodowski, Mandy. "Pedagogical content knowledge of sustainability." International Journal of Sustainability in Higher Education 18, no. 6 (September 4, 2017): 841–56. http://dx.doi.org/10.1108/ijshe-02-2016-0035.
Повний текст джерелаAbdulkadium, Ahmed Mahdi, Raid Abd Alreda Shekan, and Haitham Ali Hussain. "Application of Data Mining and Knowledge Discovery in Medical Databases." Webology 19, no. 1 (January 20, 2022): 4912–24. http://dx.doi.org/10.14704/web/v19i1/web19329.
Повний текст джерелаДисертації з теми "Data and human knowledge learning"
McKay, Elspeth, and elspeth@rmit edu au. "Instructional strategies integrating cognitive style construct: A meta-knowledge processing model." Deakin University. School of Computing and Mathematics, 2000. http://tux.lib.deakin.edu.au./adt-VDU/public/adt-VDU20061011.122556.
Повний текст джерелаPomponio, Laura. "Definition of a human-machine learning process from timed observations : application to the modelling of human behaviourfor the detection of abnormal behaviour of old people at home." Thesis, Aix-Marseille, 2012. http://www.theses.fr/2012AIXM4358.
Повний текст джерелаKnowledge acquisition has been traditionally approached from a primarily people-driven perspective, through Knowledge Engineering and Management, or from a primarily data-driven approach, through Knowledge Discovery in Databases, rather than from an integral standpoint. This thesis proposes then a human-machine learning approach that combines a Knowledge Engineering modelling approach called TOM4D (Timed Observation Modelling For Diagnosis) with a process of Knowledge Discovery in Databases based on an automatic data mining technique called TOM4L (Timed Observation Mining For Learning). The combination and comparison between models obtained through TOM4D and those ones obtained through TOM4L is possible, owing to that TOM4D and TOM4L are based on the Theory of Timed Observations and share the same representation formalism. Consequently, a learning process nourished with experts' knowledge and knowledge discovered in data is defined in the present work. In addition, this dissertation puts forward a theoretical framework of abstraction levels, in line with the mentioned theory and inspired by the Newell's Knowledge Level work, in order to reduce the broad gap of semantic content that exists between data, relative to an observed process, in a database and what can be inferred in a higher level; that is, in the experts' discursive level. Thus, the human-machine learning approach along with the notion of abstraction levels are then applied to the modelling of human behaviour in smart environments. In particular, the modelling of elderly people's behaviour at home in the GerHome Project of the CSTB (Centre Scientifique et Technique du Bâtiment) of Sophia Antipolis, France
Gaspar, Paulo Miguel da Silva. "Computational methods for gene characterization and genomic knowledge extraction." Doctoral thesis, Universidade de Aveiro, 2014. http://hdl.handle.net/10773/13949.
Повний текст джерелаMotivation: Medicine and health sciences are changing from the classical symptom-based to a more personalized and genetics-based paradigm, with an invaluable impact in health-care. While advancements in genetics were already contributing significantly to the knowledge of the human organism, the breakthrough achieved by several recent initiatives provided a comprehensive characterization of the human genetic differences, paving the way for a new era of medical diagnosis and personalized medicine. Data generated from these and posterior experiments are now becoming available, but its volume is now well over the humanly feasible to explore. It is then the responsibility of computer scientists to create the means for extracting the information and knowledge contained in that data. Within the available data, genetic structures contain significant amounts of encoded information that has been uncovered in the past decades. Finding, reading and interpreting that information are necessary steps for building computational models of genetic entities, organisms and diseases; a goal that in due course leads to human benefits. Aims: Numerous patterns can be found within the human variome and exome. Exploring these patterns enables the computational analysis and manipulation of digital genomic data, but requires specialized algorithmic approaches. In this work we sought to create and explore efficient methodologies to computationally calculate and combine known biological patterns for various purposes, such as the in silico optimization of genetic structures, analysis of human genes, and prediction of pathogenicity from human genetic variants. Results: We devised several computational strategies to evaluate genes, explore genomes, manipulate sequences, and analyze patients’ variomes. By resorting to combinatorial and optimization techniques we were able to create and combine sequence redesign algorithms to control genetic structures; by combining the access to several web-services and external resources we created tools to explore and analyze available genetic data and patient data; and by using machine learning we developed a workflow for analyzing human mutations and predicting their pathogenicity.
Motivação: A medicina e as ciências da saúde estão atualmente num processo de alteração que muda o paradigma clássico baseado em sintomas para um personalizado e baseado na genética. O valor do impacto desta mudança nos cuidados da saúde é inestimável. Não obstante as contribuições dos avanços na genética para o conhecimento do organismo humano até agora, as descobertas realizadas recentemente por algumas iniciativas forneceram uma caracterização detalhada das diferenças genéticas humanas, abrindo o caminho a uma nova era de diagnóstico médico e medicina personalizada. Os dados gerados por estas e outras iniciativas estão disponíveis mas o seu volume está muito para lá do humanamente explorável, e é portanto da responsabilidade dos cientistas informáticos criar os meios para extrair a informação e conhecimento contidos nesses dados. Dentro dos dados disponíveis estão estruturas genéticas que contêm uma quantidade significativa de informação codificada que tem vindo a ser descoberta nas últimas décadas. Encontrar, ler e interpretar essa informação são passos necessários para construir modelos computacionais de entidades genéticas, organismos e doenças; uma meta que, em devido tempo, leva a benefícios humanos. Objetivos: É possível encontrar vários padrões no varioma e exoma humano. Explorar estes padrões permite a análise e manipulação computacional de dados genéticos digitais, mas requer algoritmos especializados. Neste trabalho procurámos criar e explorar metodologias eficientes para o cálculo e combinação de padrões biológicos conhecidos, com a intenção de realizar otimizações in silico de estruturas genéticas, análises de genes humanos, e previsão da patogenicidade a partir de diferenças genéticas humanas. Resultados: Concebemos várias estratégias computacionais para avaliar genes, explorar genomas, manipular sequências, e analisar o varioma de pacientes. Recorrendo a técnicas combinatórias e de otimização criámos e conjugámos algoritmos de redesenho de sequências para controlar estruturas genéticas; através da combinação do acesso a vários web-services e recursos externos criámos ferramentas para explorar e analisar dados genéticos, incluindo dados de pacientes; e através da aprendizagem automática desenvolvemos um procedimento para analisar mutações humanas e prever a sua patogenicidade.
Zeni, Mattia. "Bridging Sensor Data Streams and Human Knowledge." Doctoral thesis, University of Trento, 2017. http://eprints-phd.biblio.unitn.it/2724/1/Thesis.pdf.
Повний текст джерелаZhang, Ping. "Learning from Multiple Knowledge Sources." Diss., Temple University Libraries, 2013. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/214795.
Повний текст джерелаPh.D.
In supervised learning, it is usually assumed that true labels are readily available from a single annotator or source. However, recent advances in corroborative technology have given rise to situations where the true label of the target is unknown. In such problems, multiple sources or annotators are often available that provide noisy labels of the targets. In these multi-annotator problems, building a classifier in the traditional single-annotator manner, without regard for the annotator properties may not be effective in general. In recent years, how to make the best use of the labeling information provided by multiple annotators to approximate the hidden true concept has drawn the attention of researchers in machine learning and data mining. In our previous work, a probabilistic method (i.e., MAP-ML algorithm) of iteratively evaluating the different annotators and giving an estimate of the hidden true labels is developed. However, the method assumes the error rate of each annotator is consistent across all the input data. This is an impractical assumption in many cases since annotator knowledge can fluctuate considerably depending on the groups of input instances. In this dissertation, one of our proposed methods, GMM-MAPML algorithm, follows MAP-ML but relaxes the data-independent assumption, i.e., we assume an annotator may not be consistently accurate across the entire feature space. GMM-MAPML uses a Gaussian mixture model (GMM) and Bayesian information criterion (BIC) to find the fittest model to approximate the distribution of the instances. Then the maximum a posterior (MAP) estimation of the hidden true labels and the maximum-likelihood (ML) estimation of quality of multiple annotators at each Gaussian component are provided alternately. Recent studies show that it is not the case that employing more annotators regardless of their expertise will result in improved highest aggregating performance. In this dissertation, we also propose a novel algorithm to integrate multiple annotators by Aggregating Experts and Filtering Novices, which we call AEFN. AEFN iteratively evaluates annotators, filters the low-quality annotators, and re-estimates the labels based only on information obtained from the good annotators. The noisy annotations we integrate are from any combination of human and previously existing machine-based classifiers, and thus AEFN can be applied to many real-world problems. Emotional speech classification, CASP9 protein disorder prediction, and biomedical text annotation experiments show a significant performance improvement of the proposed methods (i.e., GMM-MAPML and AEFN) as compared to the majority voting baseline and the previous data-independent MAP-ML method. Recent experiments include predicting novel drug indications (i.e., drug repositioning) for both approved drugs and new molecules by integrating multiple chemical, biological or phenotypic data sources.
Temple University--Theses
Lazzarini, Nicola. "Knowledge extraction from biomedical data using machine learning." Thesis, University of Newcastle upon Tyne, 2017. http://hdl.handle.net/10443/3839.
Повний текст джерелаLipton, Zachary C. "Learning from Temporally-Structured Human Activities Data." Thesis, University of California, San Diego, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10683703.
Повний текст джерелаDespite the extraordinary success of deep learning on diverse problems, these triumphs are too often confined to large, clean datasets and well-defined objectives. Face recognition systems train on millions of perfectly annotated images. Commercial speech recognition systems train on thousands of hours of painstakingly-annotated data. But for applications addressing human activity, data can be noisy, expensive to collect, and plagued by missing values. In electronic health records, for example, each attribute might be observed on a different time scale. Complicating matters further, deciding precisely what objective warrants optimization requires critical consideration of both algorithms and the application domain. Moreover, deploying human-interacting systems requires careful consideration of societal demands such as safety, interpretability, and fairness.
The aim of this thesis is to address the obstacles to mining temporal patterns in human activity data. The primary contributions are: (1) the first application of RNNs to multivariate clinical time series data, with several techniques for bridging long-term dependencies and modeling missing data; (2) a neural network algorithm for forecasting surgery duration while simultaneously modeling heteroscedasticity; (3) an approach to quantitative investing that uses RNNs to forecast company fundamentals; (4) an exploration strategy for deep reinforcement learners that significantly speeds up dialogue policy learning; (5) an algorithm to minimize the number of catastrophic mistakes made by a reinforcement learner; (6) critical works addressing model interpretability and fairness in algorithmic decision-making.
Varol, Gül. "Learning human body and human action representations from visual data." Thesis, Paris Sciences et Lettres (ComUE), 2019. http://www.theses.fr/2019PSLEE029.
Повний текст джерелаThe focus of visual content is often people. Automatic analysis of people from visual data is therefore of great importance for numerous applications in content search, autonomous driving, surveillance, health care, and entertainment. The goal of this thesis is to learn visual representations for human understanding. Particular emphasis is given to two closely related areas of computer vision: human body analysis and human action recognition. In summary, our contributions are the following: (i) we generate photo-realistic synthetic data for people that allows training CNNs for human body analysis, (ii) we propose a multi-task architecture to recover a volumetric body shape from a single image, (iii) we study the benefits of long-term temporal convolutions for human action recognition using 3D CNNs, (iv) we incorporate similarity training in multi-view videos to design view-independent representations for action recognition
Kaithi, Bhargavacharan Reddy. "Knowledge Graph Reasoning over Unseen RDF Data." Wright State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=wright1571955816559707.
Повний текст джерелаToussaint, Ben-Manson. "Apprentissage automatique à partir de traces multi-sources hétérogènes pour la modélisation de connaissances perceptivo-gestuelles." Thesis, Université Grenoble Alpes (ComUE), 2015. http://www.theses.fr/2015GREAM063/document.
Повний текст джерелаPerceptual-gestural knowledge is multimodal : they combine theoretical and perceptual and gestural knowledge. It is difficult to capture in Intelligent Tutoring Systems. In fact, its capture in such systems involves the use of multiple devices or sensors covering all the modalities of underlying interactions. The "traces" of these interactions -also referred to as "activity traces"- are the raw material for the production of key tutoring services that consider their multimodal nature. Methods for "learning analytics" and production of "tutoring services" that favor one or another facet over others, are incomplete. However, the use of diverse devices generates heterogeneous activity traces. Those latter are hard to model and treat.My doctoral project addresses the challenge related to the production of tutoring services that are congruent to this type of knowledge. I am specifically interested to this type of knowledge in the context of "ill-defined domains". My research case study is the Intelligent Tutoring System TELEOS, a simulation platform dedicated to percutaneous orthopedic surgery.The contributions of this thesis are threefold : (1) the formalization of perceptual-gestural interactions sequences; (2) the implementation of tools capable of reifying the proposed conceptual model; (3) the conception and implementation of algorithmic tools fostering the analysis of these sequences from a didactic point of view
Книги з теми "Data and human knowledge learning"
Stefanie, Lindstaedt, Kloos Carlos Delgado, Hernández-Leo Davinia, and SpringerLink (Online service), eds. 21st Century Learning for 21st Century Skills: 7th European Conference of Technology Enhanced Learning, EC-TEL 2012, Saarbrücken, Germany, September 18-21, 2012. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.
Знайти повний текст джерелаSpiliopoulou, Myra, Lars Schmidt-Thieme, and Ruth Janning, eds. Data Analysis, Machine Learning and Knowledge Discovery. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-01595-8.
Повний текст джерелаLausen, Berthold, Sabine Krolak-Schwerdt, and Matthias Böhmer, eds. Data Science, Learning by Latent Structures, and Knowledge Discovery. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-44983-7.
Повний текст джерелаChadha, Gaurav. e-Learning: An expression of the knowledge economy. New Delhi: Tata McGraw-Hill, 2002.
Знайти повний текст джерелаChadha, Gaurav. e-Learning: An expression of the knowledge economy. New Delhi: Tata McGraw-Hill, 2002.
Знайти повний текст джерелаConference on Data Analysis, Learning Symbolic and Numeric Knowledge (1989 Antibes, France). Data analysis, learning symbolic and numeric knowledge: Proceedings of the Conference on Data Analysis, Learning Symbolic and Numeric Knowledge, Antibes, September 11-14, 1989. Commack, N.Y: Nova Science Publishers, 1989.
Знайти повний текст джерелаE, Diday, and Institut national de recherche en informatique et en automatique (France), eds. Data analysis, learning symbolic and numeric knowledge: Proceedings of the Conference on Data Analysis, Learning Symbolic and Numeric Knowledge, Antibes, September 11-14, 1989. Commack, N.Y: Nova Science Publishers, 1989.
Знайти повний текст джерелаSallis, Edward. Knowledge management in education: Enhancing learning & education. London: Kogan Page, 2002.
Знайти повний текст джерелаDong, Yuxiao, Nicolas Kourtellis, Barbara Hammer, and Jose A. Lozano, eds. Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86517-7.
Повний текст джерелаDong, Yuxiao, Nicolas Kourtellis, Barbara Hammer, and Jose A. Lozano, eds. Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86514-6.
Повний текст джерелаЧастини книг з теми "Data and human knowledge learning"
Oikarinen, Emilia, Kai Puolamäki, Samaneh Khoshrou, and Mykola Pechenizkiy. "Supervised Human-Guided Data Exploration." In Machine Learning and Knowledge Discovery in Databases, 85–101. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43823-4_8.
Повний текст джерелаMitchell, Tom. "Machine Learning for Analyzing Human Brain Function." In Advances in Knowledge Discovery and Data Mining, 1. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11430919_1.
Повний текст джерелаHenelius, Andreas, Emilia Oikarinen, and Kai Puolamäki. "Tiler: Software for Human-Guided Data Exploration." In Machine Learning and Knowledge Discovery in Databases, 672–76. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-10997-4_49.
Повний текст джерелаFreitas, Leandro O., Pedro R. Henriques, and Paulo Novais. "Knowledge Inference Through Analysis of Human Activities." In Intelligent Data Engineering and Automated Learning – IDEAL 2019, 274–81. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33607-3_30.
Повний текст джерелаOsmani, Aomar, Massinissa Hamidi, and Pegah Alizadeh. "Hierarchical Learning of Dependent Concepts for Human Activity Recognition." In Advances in Knowledge Discovery and Data Mining, 79–92. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75765-6_7.
Повний текст джерелаAndrade, Thiago, Brais Cancela, and João Gama. "Mining Human Mobility Data to Discover Locations and Habits." In Machine Learning and Knowledge Discovery in Databases, 390–401. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43887-6_32.
Повний текст джерелаYin, Jie, Son N. Tran, and Qing Zhang. "Human Identification via Unsupervised Feature Learning from UWB Radar Data." In Advances in Knowledge Discovery and Data Mining, 322–34. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93034-3_26.
Повний текст джерелаXu, Zhen, and Binheng Song. "A Machine Learning Application for Human Resource Data Mining Problem." In Advances in Knowledge Discovery and Data Mining, 847–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11731139_99.
Повний текст джерелаHuang, Heng, Yunhan Bai, Hongwei Liang, and Xiaozhong Liu. "IR Embedding Fairness Inspection via Contrastive Learning and Human-AI Collaborative Intelligence." In Advances in Knowledge Discovery and Data Mining, 142–53. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2238-9_11.
Повний текст джерелаMercep, Ljubo, Gernot Spiegelberg, and Alois Knoll. "Human Performance Profiling While Driving a Sidestick-Controlled Car." In Data Science, Learning by Latent Structures, and Knowledge Discovery, 455–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-44983-7_40.
Повний текст джерелаТези доповідей конференцій з теми "Data and human knowledge learning"
Reddy, Siddharth, Igor Labutov, Siddhartha Banerjee, and Thorsten Joachims. "Unbounded Human Learning." In KDD '16: The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2939672.2939850.
Повний текст джерелаFeng, Jie, Zeyu Yang, Fengli Xu, Haisu Yu, Mudan Wang, and Yong Li. "Learning to Simulate Human Mobility." In KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3394486.3412862.
Повний текст джерелаHwang, Kevin, Sai Challagundla, Maryam Alomair, Doug Janssen, Kendall Morton, Lujie Chen, and Fow-Sen Choa. "Towards the acceleration of human learning capabilities through AI-assisted knowledge-tree building." In Big Data VI: Learning, Analytics, and Applications, edited by Panos P. Markopoulos. SPIE, 2024. http://dx.doi.org/10.1117/12.3013103.
Повний текст джерелаCorvino, Gabriel, Vitor Vasconcelos Oliveira, Angelo C. Mendes da Silva, and Ricardo Marcondes Marcacini. "On the use of Query by Committee for Human-in-the-Loop Named Entity Recognition." In Symposium on Knowledge Discovery, Mining and Learning. Sociedade Brasileira de Computação - SBC, 2022. http://dx.doi.org/10.5753/kdmile.2022.227953.
Повний текст джерелаManoharan, Geetha, Jalaja V, Manoj A. Sathe, Neetika, Melanie Lourens, and K. Suresh. "Machine Learning and Data Privacy in Human Resource Management." In 2023 4th International Conference on Computation, Automation and Knowledge Management (ICCAKM). IEEE, 2023. http://dx.doi.org/10.1109/iccakm58659.2023.10449576.
Повний текст джерелаGuestrin, Carlos. "4 Perspectives in Human-Centered Machine Learning." In KDD '19: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3292500.3340399.
Повний текст джерелаCha, Inha, Juhyun Oh, Cheul Young Park, Jiyoon Han, and Hwalsuk Lee. "Unlocking the Tacit Knowledge of Data Work in Machine Learning." In CHI '23: CHI Conference on Human Factors in Computing Systems. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3544549.3585616.
Повний текст джерелаSaisho, Osamu, Takeshi Ohguro, Jingyu Sun, Hiroshi Imamura, Susumu Takeuchi, and Daigoro Yokozeki. "Human Knowledge Based Efficient Interactive Data Annotation via Active Weakly Supervised Learning." In 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops). IEEE, 2021. http://dx.doi.org/10.1109/percomworkshops51409.2021.9431067.
Повний текст джерелаNasiriyan, Fariba, and Hassan Khotanlou. "Human detection in laser range data using deep learning and 3-D objects." In 2015 7th Conference on Information and Knowledge Technology (IKT). IEEE, 2015. http://dx.doi.org/10.1109/ikt.2015.7288748.
Повний текст джерелаZhang, Dongtian, Weiwei Tian, Yifan Yin, Xiufeng Liu, Xu Cheng, and Fan Shi. "Human Knowledge-based Compressed Federated Learning Model for Wind Turbine Blade Icing Detection." In 2022 International Conference on High Performance Big Data and Intelligent Systems (HDIS). IEEE, 2022. http://dx.doi.org/10.1109/hdis56859.2022.9991642.
Повний текст джерелаЗвіти організацій з теми "Data and human knowledge learning"
Shrestha, Tanuja, Mir A. Matin, Vishwas Chitale, and Samuel Thomas. Exploring the potential of deep learning for classifying camera trap data: A case study from Nepal - working paper. International Centre for Integrated Mountain Development (ICIMOD), September 2023. http://dx.doi.org/10.53055/icimod.1016.
Повний текст джерелаWay, L., S. West, B. Swift, L. Whatford, and C. Rymer. Learnings from the pilot Citizen Science and AMR project. Food Standards Agency, November 2023. http://dx.doi.org/10.46756/sci.fsa.axj107.
Повний текст джерелаBelokonova, Nadezhda, Elena Ermishina, Natalya Kataeva, Natalia Naronova, and Kristina Golitsyna. E-learning course "Chemistry". SIB-Expertise, January 2024. http://dx.doi.org/10.12731/er0770.29012024.
Повний текст джерелаRau, Jane. PR-580-163710-R01 Determining the Impact of Human Factors in the Performance of In-Service NDE. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), January 2020. http://dx.doi.org/10.55274/r0011651.
Повний текст джерелаKataeva, Natalya, Natalia Naronova, and Kristina Golitsyna. E-learning course "Bioorganic chemistry". Федеральное государственное бюджетное образовательное учреждение высшего образования "Уральский государственный медицинский университет" Министерства здравоохранения Российской Федерации, December 2024. https://doi.org/10.12731/er0857.12122024.
Повний текст джерелаOskolkov, Nikolay. Machine Learning for Computational Biology. Instats Inc., 2024. http://dx.doi.org/10.61700/l01vi14ohm8en1490.
Повний текст джерелаFang, Mei Lan, Lupin Battersby, Marianne Cranwell, Heather Cassie, Moya Fox, Philippa Sterlini, Jenna Breckenridge, Alex Gardner, and Thomas Curtin. IKT for Research Stage 5: Data Collection. University of Dundee, December 2022. http://dx.doi.org/10.20933/100001252.
Повний текст джерелаFang, Mei Lan, Lupin Battersby, Marianne Cranwell, Heather Cassie, Moya Fox, Philippa Sterlini, Jenna Breckenridge, Alex Gardner, and Thomas Curtin. IKT for Research Stage 6: Data Analysis. University of Dundee, December 2022. http://dx.doi.org/10.20933/100001253.
Повний текст джерелаEngel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, July 1996. http://dx.doi.org/10.32747/1996.7613033.bard.
Повний текст джерелаMillican, Juliet. Civil Society Learning Journey Briefing Note 3: Methods for Supporting or Countering Informal Social Movements. Institute of Development Studies, October 2022. http://dx.doi.org/10.19088/k4d.2022.153.
Повний текст джерела