Letteratura scientifica selezionata sul tema "Data and human knowledge learning"
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Articoli di riviste sul tema "Data and human knowledge learning"
Dudyrev, Egor, Ilia Semenkov, Sergei O. Kuznetsov, Gleb Gusev, Andrew Sharp e Oleg S. Pianykh. "Human knowledge models: Learning applied knowledge from the data". PLOS ONE 17, n. 10 (20 ottobre 2022): e0275814. http://dx.doi.org/10.1371/journal.pone.0275814.
Testo completoWeber, Patrick, Nicolas Weber, Michael Goesele e Rüdiger Kabst. "Prospect for Knowledge in Survey Data". Social Science Computer Review 36, n. 5 (12 settembre 2017): 575–90. http://dx.doi.org/10.1177/0894439317725836.
Testo completoYao, Quanming. "Towards Human-like Learning from Relational Structured Data". Proceedings of the AAAI Conference on Artificial Intelligence 38, n. 20 (24 marzo 2024): 22684. http://dx.doi.org/10.1609/aaai.v38i20.30300.
Testo completoKulikovskikh, Ilona, Tomislav Lipic e Tomislav Šmuc. "From Knowledge Transmission to Knowledge Construction: A Step towards Human-Like Active Learning". Entropy 22, n. 8 (18 agosto 2020): 906. http://dx.doi.org/10.3390/e22080906.
Testo completoAnderson, John R. "Methodologies for studying human knowledge". Behavioral and Brain Sciences 10, n. 3 (settembre 1987): 467–77. http://dx.doi.org/10.1017/s0140525x00023554.
Testo completoKwak, Beong-woo, Youngwook Kim, Yu Jin Kim, Seung-won Hwang e Jinyoung Yeo. "TrustAL: Trustworthy Active Learning Using Knowledge Distillation". Proceedings of the AAAI Conference on Artificial Intelligence 36, n. 7 (28 giugno 2022): 7263–71. http://dx.doi.org/10.1609/aaai.v36i7.20688.
Testo completoAngrist, Noam, Simeon Djankov, Pinelopi K. Goldberg e Harry A. Patrinos. "Measuring human capital using global learning data". Nature 592, n. 7854 (10 marzo 2021): 403–8. http://dx.doi.org/10.1038/s41586-021-03323-7.
Testo completoKalaycı, Tahir Emre, Bor Bricelj, Marko Lah, Franz Pichler, Matthias K. Scharrer e Jelena Rubeša-Zrim. "A Knowledge Graph-Based Data Integration Framework Applied to Battery Data Management". Sustainability 13, n. 3 (2 febbraio 2021): 1583. http://dx.doi.org/10.3390/su13031583.
Testo completoSinger-Brodowski, Mandy. "Pedagogical content knowledge of sustainability". International Journal of Sustainability in Higher Education 18, n. 6 (4 settembre 2017): 841–56. http://dx.doi.org/10.1108/ijshe-02-2016-0035.
Testo completoAbdulkadium, Ahmed Mahdi, Raid Abd Alreda Shekan e Haitham Ali Hussain. "Application of Data Mining and Knowledge Discovery in Medical Databases". Webology 19, n. 1 (20 gennaio 2022): 4912–24. http://dx.doi.org/10.14704/web/v19i1/web19329.
Testo completoTesi sul tema "Data and human knowledge learning"
McKay, Elspeth, e 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.
Testo completoPomponio, 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.
Testo completoKnowledge 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.
Testo completoMotivation: 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.
Testo completoZhang, Ping. "Learning from Multiple Knowledge Sources". Diss., Temple University Libraries, 2013. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/214795.
Testo completoPh.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.
Testo completoLipton, Zachary C. "Learning from Temporally-Structured Human Activities Data". Thesis, University of California, San Diego, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10683703.
Testo completoDespite 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.
Testo completoThe 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.
Testo completoToussaint, 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.
Testo completoPerceptual-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
Libri sul tema "Data and human knowledge learning"
Stefanie, Lindstaedt, Kloos Carlos Delgado, Hernández-Leo Davinia e SpringerLink (Online service), a cura di. 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.
Cerca il testo completoSpiliopoulou, Myra, Lars Schmidt-Thieme e Ruth Janning, a cura di. Data Analysis, Machine Learning and Knowledge Discovery. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-01595-8.
Testo completoLausen, Berthold, Sabine Krolak-Schwerdt e Matthias Böhmer, a cura di. 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.
Testo completoChadha, Gaurav. e-Learning: An expression of the knowledge economy. New Delhi: Tata McGraw-Hill, 2002.
Cerca il testo completoChadha, Gaurav. e-Learning: An expression of the knowledge economy. New Delhi: Tata McGraw-Hill, 2002.
Cerca il testo completoConference 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.
Cerca il testo completoE, Diday, e Institut national de recherche en informatique et en automatique (France), a cura di. 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.
Cerca il testo completoSallis, Edward. Knowledge management in education: Enhancing learning & education. London: Kogan Page, 2002.
Cerca il testo completoDong, Yuxiao, Nicolas Kourtellis, Barbara Hammer e Jose A. Lozano, a cura di. 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.
Testo completoDong, Yuxiao, Nicolas Kourtellis, Barbara Hammer e Jose A. Lozano, a cura di. 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.
Testo completoCapitoli di libri sul tema "Data and human knowledge learning"
Oikarinen, Emilia, Kai Puolamäki, Samaneh Khoshrou e 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.
Testo completoMitchell, 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.
Testo completoHenelius, Andreas, Emilia Oikarinen e 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.
Testo completoFreitas, Leandro O., Pedro R. Henriques e 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.
Testo completoOsmani, Aomar, Massinissa Hamidi e 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.
Testo completoAndrade, Thiago, Brais Cancela e 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.
Testo completoYin, Jie, Son N. Tran e 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.
Testo completoXu, Zhen, e 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.
Testo completoHuang, Heng, Yunhan Bai, Hongwei Liang e 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.
Testo completoMercep, Ljubo, Gernot Spiegelberg e 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.
Testo completoAtti di convegni sul tema "Data and human knowledge learning"
Reddy, Siddharth, Igor Labutov, Siddhartha Banerjee e 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.
Testo completoFeng, Jie, Zeyu Yang, Fengli Xu, Haisu Yu, Mudan Wang e 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.
Testo completoHwang, Kevin, Sai Challagundla, Maryam Alomair, Doug Janssen, Kendall Morton, Lujie Chen e Fow-Sen Choa. "Towards the acceleration of human learning capabilities through AI-assisted knowledge-tree building". In Big Data VI: Learning, Analytics, and Applications, a cura di Panos P. Markopoulos. SPIE, 2024. http://dx.doi.org/10.1117/12.3013103.
Testo completoCorvino, Gabriel, Vitor Vasconcelos Oliveira, Angelo C. Mendes da Silva e 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.
Testo completoManoharan, Geetha, Jalaja V, Manoj A. Sathe, Neetika, Melanie Lourens e 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.
Testo completoGuestrin, 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.
Testo completoCha, Inha, Juhyun Oh, Cheul Young Park, Jiyoon Han e 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.
Testo completoSaisho, Osamu, Takeshi Ohguro, Jingyu Sun, Hiroshi Imamura, Susumu Takeuchi e 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.
Testo completoNasiriyan, Fariba, e 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.
Testo completoZhang, Dongtian, Weiwei Tian, Yifan Yin, Xiufeng Liu, Xu Cheng e 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.
Testo completoRapporti di organizzazioni sul tema "Data and human knowledge learning"
Shrestha, Tanuja, Mir A. Matin, Vishwas Chitale e 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), settembre 2023. http://dx.doi.org/10.53055/icimod.1016.
Testo completoWay, L., S. West, B. Swift, L. Whatford e C. Rymer. Learnings from the pilot Citizen Science and AMR project. Food Standards Agency, novembre 2023. http://dx.doi.org/10.46756/sci.fsa.axj107.
Testo completoBelokonova, Nadezhda, Elena Ermishina, Natalya Kataeva, Natalia Naronova e Kristina Golitsyna. E-learning course "Chemistry". SIB-Expertise, gennaio 2024. http://dx.doi.org/10.12731/er0770.29012024.
Testo completoRau, 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), gennaio 2020. http://dx.doi.org/10.55274/r0011651.
Testo completoKataeva, Natalya, Natalia Naronova e Kristina Golitsyna. E-learning course "Bioorganic chemistry". Федеральное государственное бюджетное образовательное учреждение высшего образования "Уральский государственный медицинский университет" Министерства здравоохранения Российской Федерации, dicembre 2024. https://doi.org/10.12731/er0857.12122024.
Testo completoOskolkov, Nikolay. Machine Learning for Computational Biology. Instats Inc., 2024. http://dx.doi.org/10.61700/l01vi14ohm8en1490.
Testo completoFang, Mei Lan, Lupin Battersby, Marianne Cranwell, Heather Cassie, Moya Fox, Philippa Sterlini, Jenna Breckenridge, Alex Gardner e Thomas Curtin. IKT for Research Stage 5: Data Collection. University of Dundee, dicembre 2022. http://dx.doi.org/10.20933/100001252.
Testo completoFang, Mei Lan, Lupin Battersby, Marianne Cranwell, Heather Cassie, Moya Fox, Philippa Sterlini, Jenna Breckenridge, Alex Gardner e Thomas Curtin. IKT for Research Stage 6: Data Analysis. University of Dundee, dicembre 2022. http://dx.doi.org/10.20933/100001253.
Testo completoEngel, Bernard, Yael Edan, James Simon, Hanoch Pasternak e Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, luglio 1996. http://dx.doi.org/10.32747/1996.7613033.bard.
Testo completoMillican, Juliet. Civil Society Learning Journey Briefing Note 3: Methods for Supporting or Countering Informal Social Movements. Institute of Development Studies, ottobre 2022. http://dx.doi.org/10.19088/k4d.2022.153.
Testo completo