Littérature scientifique sur le sujet « Machine learnings »
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Articles de revues sur le sujet "Machine learnings"
Li, Tianshu. « Fintech Application in Banking Operations - Application of Machine Learning in Mitigating Bank Derivatives Counterparty Risks ». Asian Business Research 4, no 3 (8 octobre 2019) : 1. http://dx.doi.org/10.20849/abr.v4i3.652.
Texte intégralMakarov, Vladimir, Christophe Chabbert, Elina Koletou, Fotis Psomopoulos, Natalja Kurbatova, Samuel Ramirez, Chas Nelson, Prashant Natarajan et Bikalpa Neupane. « Good machine learning practices : Learnings from the modern pharmaceutical discovery enterprise ». Computers in Biology and Medicine 177 (juillet 2024) : 108632. http://dx.doi.org/10.1016/j.compbiomed.2024.108632.
Texte intégralKim, Jin Kook. « A Study on the Estimation Model for the Visitors to Let’s Run Park Using Machine Learning ». Korean Journal of Sport Science 32, no 3 (30 septembre 2021) : 411–18. http://dx.doi.org/10.24985/kjss.2021.32.3.411.
Texte intégralMalik, Sehrish, et DoHyeun Kim. « Improved Control Scheduling Based on Learning to Prediction Mechanism for Efficient Machine Maintenance in Smart Factory ». Actuators 10, no 2 (31 janvier 2021) : 27. http://dx.doi.org/10.3390/act10020027.
Texte intégralPREETHAM S, M C CHANDRASHEKHAR et M Z KURIAN. « METHODOLOGY FOR IMPLEMENTATION OF PREDICTION MODEL FOR STUDENTS USING MACHINE LEARNING ». international journal of engineering technology and management sciences 7, no 3 (2023) : 764–66. http://dx.doi.org/10.46647/ijetms.2023.v07i03.116.
Texte intégralKurniawan, Robi, et Shunsuke Managi. « Forecasting annual energy consumption using machine learnings : Case of Indonesia ». IOP Conference Series : Earth and Environmental Science 257 (10 mai 2019) : 012032. http://dx.doi.org/10.1088/1755-1315/257/1/012032.
Texte intégralSingh, Priyanka, Chakshu Garg, Aman Namdeo, Krishna Mohan Agarwal et Rajesh Kumar Rai. « Development of Prediction models for Bond Strength of Steel Fiber Reinforced Concrete by Computational Machine Learning ». E3S Web of Conferences 220 (2020) : 01097. http://dx.doi.org/10.1051/e3sconf/202022001097.
Texte intégralDas, Aditi. « Automatic Personality Identification using Machine Learning ». International Journal for Research in Applied Science and Engineering Technology 9, no VI (30 juin 2021) : 3528–34. http://dx.doi.org/10.22214/ijraset.2021.35386.
Texte intégralMalinda Sari Sembiring, Windi Astuti, Iskandar Muda,. « The Influence of Cloud Computing, Artificial Intelligence, Machine Learnings and Digital Disruption on the Design of Accounting and Finance Functions Mediated by Data Processing ». International Journal on Recent and Innovation Trends in Computing and Communication 11, no 11 (30 novembre 2023) : 56–62. http://dx.doi.org/10.17762/ijritcc.v11i11.9087.
Texte intégralSendak, Mark P., William Ratliff, Dina Sarro, Elizabeth Alderton, Joseph Futoma, Michael Gao, Marshall Nichols et al. « Real-World Integration of a Sepsis Deep Learning Technology Into Routine Clinical Care : Implementation Study ». JMIR Medical Informatics 8, no 7 (15 juillet 2020) : e15182. http://dx.doi.org/10.2196/15182.
Texte intégralThèses sur le sujet "Machine learnings"
Algohary, Ahmad. « PROSTATE CANCER RISK STRATIFICATION USING RADIOMICS FOR PATIENTS ON ACTIVE SURVEILLANCE : MULTI-INSTITUTIONAL USE CASES ». Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1599231033923829.
Texte intégralStohr, Daniel Christoph [Verfasser]. « Die beruflichen Anforderungen der Digitalisierung hinsichtlich formaler, physischer und kompetenzspezifischer Aspekte : Eine Analyse von Stellenanzeigen mittels Methoden des Text Minings und Machine Learnings / Daniel Christoph Stohr ». Frankfurt a.M. : Peter Lang GmbH, Internationaler Verlag der Wissenschaften, 2019. http://d-nb.info/1185347240/34.
Texte intégralTebbifakhr, Amirhossein. « Machine Translation For Machines ». Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/320504.
Texte intégralDinakar, Karthik. « Lensing Machines : representing perspective in machine learning ». Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/112523.
Texte intégralCataloged from PDF version of thesis. Due to the condition of the original material with text runs off the edges of the pages, the reproduction may have unavoidable flaws.
Includes bibliographical references (pages 167-172).
Generative models are venerated as full probabilistic models that randomly generate observable data given a set of latent variables that cannot be directly observed. They can be used to simulate values for variables in the model, allowing analysis by synthesis or model criticism, towards an iterative cycle of model specification, estimation, and critique. However, many datasets represent a combination of several viewpoints - different ways of looking at the same data that leads to various generalizations. For example, a corpus that has data generated by multiple people may be mixtures of several perspectives and can be viewed with different opinions by others. It isn't always possible to represent the viewpoints by clean separation, in advance, of examples representing each perspective and train a separate model for each point of view. In this thesis, we introduce lensing, a mixed-initiative technique to (i) extract lenses or mappings between machine-learned representations and perspectives of human experts, and (2) generate lensed models that afford multiple perspectives of the same dataset. We explore lensing of latent variable model in their configuration, parameter and evidential spaces. We apply lensing to three health applications, namely imbuing the perspectives of experts into latent variable models that analyze adolescent distress and crisis counseling.
by Karthik Dinakar.
Ph. D.
Roderus, Jens, Simon Larson et Eric Pihl. « Hadoop scalability evaluation for machine learning algorithms on physical machines : Parallel machine learning on computing clusters ». Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-20102.
Texte intégralKent, W. F. « Machine learning for parameter identification of electric induction machines ». Thesis, University of Liverpool, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.399178.
Texte intégralThorén, Daniel. « Radar based tank level measurement using machine learning : Agricultural machines ». Thesis, Linköpings universitet, Programvara och system, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176259.
Texte intégralRomano, Donato. « Machine Learning algorithms for predictive diagnostics applied to automatic machines ». Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/22319/.
Texte intégralSchneider, C. « Using unsupervised machine learning for fault identification in virtual machines ». Thesis, University of St Andrews, 2015. http://hdl.handle.net/10023/7327.
Texte intégralSOAVE, Elia. « Diagnostics and prognostics of rotating machines through cyclostationary methods and machine learning ». Doctoral thesis, Università degli studi di Ferrara, 2022. http://hdl.handle.net/11392/2490999.
Texte intégralNegli ultimi decenni, l’analisi vibrazionale è stata sfruttata per il monitoraggio di molti sistemi meccanici per applicazioni industriali. Nonostante molte pubblicazioni abbiano dimostrato come la diagnostica vibrazionale possa raggiungere risultati soddisfacenti, lo scenario industriale odierno è in profondo cambiamento, guidato dalla necessità di ridurre tempi e costi produttivi. In questa direzione, la ricerca deve concentrarsi sul miglioramento dell’efficienza computazionale delle tecniche di analisi del segnale applicate a fini diagnostici. Allo stesso modo, il mondo industriale richiede una sempre maggior attenzione per la manutenzione predittiva, al fine di stimare l’effettivo danneggiamento del sistema evitando così inutili fermi macchina per operazioni manutentive. In tale ambito, negli ultimi anni l’attività di ricerca si sta spostando verso lo sviluppo di modelli prognostici finalizzati alla stima della vita utile residua dei componenti. Tuttavia, è importante ricordare come i due ambiti siano strettamente connessi, essendo la diagnostica la base su cui fondare l’efficacia di ciascun modello prognostico. Su questa base, questa tesi è stata incentrata su queste due diverse, ma tra loro connesse, aree al fine di identificare e predire possibile cause di cedimento su macchine rotanti per applicazioni industriali. La prima parte della tesi è concentrata sullo sviluppo di un nuovo indicatore di blind deconvolution per l’identificazione di difetti su organi rotanti sulla base della teoria ciclostazionaria. Il criterio presentato vuole andare a ridurre il costo computazionale richiesto dalla blind deconvolution tramite l’utilizzo della serie di Fourier-Bessel grazie alla sua natura modulata, maggiormente affine alla tipica firma vibratoria del difetto. L’indicatore proposto viene accuratamente confrontato con il suo analogo basato sulla classica serie di Fourier considerando sia segnali simulati che segnali di vibrazione reali. Il confronto vuole dimostrare il miglioramento fornito dal nuovo criterio in termini sia di minor numero di operazioni richieste dall’algoritmo che di efficacia diagnostica anche in condizioni di segnale molto rumoroso. Il contributo innovativo di questa parte riguarda la combinazione di ciclostazionarietà e serie di Furier-Bessel che porta alla definizione di un nuovo criterio di blind deconvolution in grado di mantenere l’efficacia diagnostica della ciclostazionarietà ma con un minor tempo computazionale per venire incontro alle richieste del mondo industriale. La second parte riguarda la definizione di un nuovo modello prognostico, appartenente alla famiglia degli hidden Markov models, costruito partendo da una distribuzione Gaussiana generalizzata. L’obbiettivo del metodo proposto è una miglior riproduzione della reale distribuzione dei dati, in particolar modo negli ultimi stadi del danneggiamento. Infatti, la comparsa e l’evoluzione del difetto comporta una modifica della distribuzione delle osservazioni fra i diversi stati. Di conseguenza, una densità di probabilità generalizzata permette la modificazione della forma della distribuzione tramite diversi valori dei parametri del modello. Il metodo proposto viene confrontato con il classico hidden Markov model di base Gaussiana in termini di qualità di riproduzione della distribuzione e predizione della sequenza di stati tramite l’analisi di alcuni test di rottura su cuscinetti volventi e sistemi complessi. L’innovatività di questa parte è data dalla definizione di un algoritmo iterativo per la stima dei parametri del modello nell’ipotesi di distribuzione Gaussiana generalizzata, sia nel caso monovariato che multivariato, partendo dalle osservazioni sul sistema fisico in esame.
Livres sur le sujet "Machine learnings"
Ertekin, Şeyda. Algorithms for efficient learning systems : Online and active learning approaches. Saarbrücken : VDM Verlag Dr. Müller, 2009.
Trouver le texte intégralCampbell, Colin. Learning with support vector machines. San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool, 2011.
Trouver le texte intégralBoyle, Brandon H. Support vector machines : Data analysis, machine learning, and applications. Hauppauge, N.Y : Nova Science Publishers, 2011.
Trouver le texte intégralZhou, Zhi-Hua. Machine Learning. Singapore : Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-1967-3.
Texte intégralJung, Alexander. Machine Learning. Singapore : Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8193-6.
Texte intégralMitchell, Tom M., Jaime G. Carbonell et Ryszard S. Michalski. Machine Learning. Boston, MA : Springer US, 1986. http://dx.doi.org/10.1007/978-1-4613-2279-5.
Texte intégralFernandes de Mello, Rodrigo, et Moacir Antonelli Ponti. Machine Learning. Cham : Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94989-5.
Texte intégralBell, Jason. Machine Learning. Indianapolis, IN, USA : John Wiley & Sons, Inc, 2014. http://dx.doi.org/10.1002/9781119183464.
Texte intégralHuang, Kaizhu, Haiqin Yang, Irwin King et Michael Lyu. Machine Learning. Berlin, Heidelberg : Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-79452-3.
Texte intégralJebara, Tony. Machine Learning. Boston, MA : Springer US, 2004. http://dx.doi.org/10.1007/978-1-4419-9011-2.
Texte intégralChapitres de livres sur le sujet "Machine learnings"
Heesen, Bernd. « Grundlagen des Machine Learnings mit R ». Dans Künstliche Intelligenz und Machine Learning mit R, 111–398. Wiesbaden : Springer Fachmedien Wiesbaden, 2023. http://dx.doi.org/10.1007/978-3-658-41576-1_6.
Texte intégralAugust, Stephanie E., et Audrey Tsaima. « Artificial Intelligence and Machine Learning : An Instructor’s Exoskeleton in the Future of Education ». Dans Innovative Learning Environments in STEM Higher Education, 79–105. Cham : Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-58948-6_5.
Texte intégralHan, Haihang, Tianjie Zhang, Qiao Dong, Xueqin Chen et Yangyang Wang. « Pavement roughness level classification based on logistic and decision tree machine learnings ». Dans Green and Intelligent Technologies for Sustainable and Smart Asphalt Pavements, 400–405. London : CRC Press, 2021. http://dx.doi.org/10.1201/9781003251125-63.
Texte intégralAwotunde, Joseph Bamidele, Sunday Adeola Ajagbe, Matthew A. Oladipupo, Jimmisayo A. Awokola, Olakunle S. Afolabi, Timothy O. Mathew et Yetunde J. Oguns. « An Improved Machine Learnings Diagnosis Technique for COVID-19 Pandemic Using Chest X-ray Images ». Dans Communications in Computer and Information Science, 319–30. Cham : Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89654-6_23.
Texte intégralBringsjord, Selmer, Naveen Sundar Govindarajulu, Shreya Banerjee et John Hummel. « Do Machine-Learning Machines Learn ? » Dans Studies in Applied Philosophy, Epistemology and Rational Ethics, 136–57. Cham : Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96448-5_14.
Texte intégralDai, Anni. « Co-creation : Space Reconfiguration by Architect and Agent Simulation Based Machine Learning ». Dans Computational Design and Robotic Fabrication, 304–13. Singapore : Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8637-6_27.
Texte intégralSödergård, Caj. « Summary of Potential and Exploitation of Big Data and AI in Bioeconomy ». Dans Big Data in Bioeconomy, 417–23. Cham : Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71069-9_32.
Texte intégralPuigbò, Jordi-Ysard, Xerxes D. Arsiwalla et Paul F. M. J. Verschure. « Challenges of Machine Learning for Living Machines ». Dans Biomimetic and Biohybrid Systems, 382–86. Cham : Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95972-6_41.
Texte intégralWehenkel, Louis A. « Machine Learning ». Dans Automatic Learning Techniques in Power Systems, 99–144. Boston, MA : Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5451-6_5.
Texte intégralCios, Krzysztof J., Witold Pedrycz et Roman W. Swiniarski. « Machine Learning ». Dans Data Mining Methods for Knowledge Discovery, 229–308. Boston, MA : Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5589-6_6.
Texte intégralActes de conférences sur le sujet "Machine learnings"
Gaber, Ayman, Mohamed Mahmoud Zaki, Ahmed Maher Mohamed et Mohamed Abdellatif Beshara. « Cellular Network Power Control Optimization Using Unsupervised Machine Learnings ». Dans 2019 International Conference on Innovative Trends in Computer Engineering (ITCE). IEEE, 2019. http://dx.doi.org/10.1109/itce.2019.8646611.
Texte intégralGuajardo, Marco, Ahmed S. Omran et Howard Clark. « Fast model-driven target optimization methods using machine learnings ». Dans Design-Technology Co-optimization XV, sous la direction de Chi-Min Yuan et Ryoung-Han Kim. SPIE, 2021. http://dx.doi.org/10.1117/12.2587122.
Texte intégralEshita, Kakeru, Kousei Nishizono, Ryusei Kunitake, Hirohumi Miyazima, Kenichi Arai et Toru Kobayashi. « Surface Roughness Prediction System for Blade Machining Using Machine Learnings ». Dans 2023 IEEE 12th Global Conference on Consumer Electronics (GCCE). IEEE, 2023. http://dx.doi.org/10.1109/gcce59613.2023.10315442.
Texte intégralPowney, M., J. Masi, D. Austin, T. Citraningtyas, M. Dyrendahl, B. Alaei, S. Cornelius, F. Dias et P. Emmet. « Legacy Learnings to Future Insight – Characterising CCUS Sites Using Legacy Data with Machine Learning ». Dans First EAGE Workshop on Hydrogen & CCS in LATAM. European Association of Geoscientists & Engineers, 2023. http://dx.doi.org/10.3997/2214-4609.202382004.
Texte intégralSato, Keita, Masafumi Chida, Yoshihiro Hayakawa et Nahomi Miyamoto Fujiki. « Automatic Feature Extraction from Wearable Sensor Data by Use of Machine Learnings ». Dans The 7th International Conference on Intelligent Systems and Image Processing 2019. The Institute of Industrial Application Engineers, 2019. http://dx.doi.org/10.12792/icisip2019.067.
Texte intégralGera, Saksham, Mr Mridul et Kireet Joshi. « Regression Analysis And Future Forecasting Of COVID-19 Using Machine Learnings Algorithm ». Dans 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence). IEEE, 2021. http://dx.doi.org/10.1109/confluence51648.2021.9377065.
Texte intégralJusman, Yessi, Muhammad Khoirul Anam, Sartika Puspita et Edwyn Saleh. « Machine Learnings of Dental Caries Images based on Hu Moment Invariants Features ». Dans 2021 International Seminar on Application for Technology of Information and Communication (iSemantic). IEEE, 2021. http://dx.doi.org/10.1109/isemantic52711.2021.9573208.
Texte intégralSrivastava, Priyank, Mainak Bandyopadhyay, Shantanu Chakraborty, Samarth Patwardhan et Huy Tran. « Classification of Wireline Formation Testing Responses Using Unsupervised Machine Learning Methods ». Dans Offshore Technology Conference. OTC, 2022. http://dx.doi.org/10.4043/31892-ms.
Texte intégralQumsiyeh, Emma, Miar Yousef et Malik Yousef. « ReScore Disease Groups Based on Multiple Machine Learnings Utilizing the Grouping-Scoring-Modeling Approach ». Dans 15th International Conference on Bioinformatics Models, Methods and Algorithms. SCITEPRESS - Science and Technology Publications, 2024. http://dx.doi.org/10.5220/0012379400003657.
Texte intégralEmery, David J., Marcelo Guarido, Brian Russell et Daniel Trad. « Machine learnings and lessons learned on improvements to Castagna’s mudrock, Gardner’s density, and Faust’s velocity estimation ». Dans Second International Meeting for Applied Geoscience & Energy. Society of Exploration Geophysicists and American Association of Petroleum Geologists, 2022. http://dx.doi.org/10.1190/image2022-3749277.1.
Texte intégralRapports d'organisations sur le sujet "Machine learnings"
Giannoulakis, Stylianos, et Arrigo Beretta. PR-471-18210-R01 Pump Failure and Performance Degradation Prediction. Chantilly, Virginia : Pipeline Research Council International, Inc. (PRCI), septembre 2020. http://dx.doi.org/10.55274/r0011801.
Texte intégralVesselinov, Velimir Valentinov. Machine Learning. Office of Scientific and Technical Information (OSTI), janvier 2019. http://dx.doi.org/10.2172/1492563.
Texte intégralValiant, L. G. Machine Learning. Fort Belvoir, VA : Defense Technical Information Center, janvier 1993. http://dx.doi.org/10.21236/ada283386.
Texte intégralChase, Melissa P. Machine Learning. Fort Belvoir, VA : Defense Technical Information Center, avril 1990. http://dx.doi.org/10.21236/ada223732.
Texte intégralKagie, Matthew J., et Park Hays. FORTE Machine Learning. Office of Scientific and Technical Information (OSTI), août 2016. http://dx.doi.org/10.2172/1561828.
Texte intégralLin, Youzuo, Shihang Feng et Esteban Rougier. Machine Learning Tutorial. Office of Scientific and Technical Information (OSTI), juillet 2022. http://dx.doi.org/10.2172/1876777.
Texte intégralCaplin, Andrew, Daniel Martin et Philip Marx. Modeling Machine Learning. Cambridge, MA : National Bureau of Economic Research, octobre 2022. http://dx.doi.org/10.3386/w30600.
Texte intégralKelly, Bryan, et Dacheng Xiu. Financial Machine Learning. Cambridge, MA : National Bureau of Economic Research, juillet 2023. http://dx.doi.org/10.3386/w31502.
Texte intégralVassilev, Apostol. Adversarial Machine Learning :. Gaithersburg, MD : National Institute of Standards and Technology, 2024. http://dx.doi.org/10.6028/nist.ai.100-2e2023.
Texte intégralJunttila, Jukka, Ville Lämsä, Leonardo Espinosa Leal et Anssi Sillanpää. Feature engineering –based machine learning models for operational state recognition of rotating machines. Peeref, mars 2023. http://dx.doi.org/10.54985/peeref.2303p8483224.
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