Literatura académica sobre el tema "Machine learnings"
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Artículos de revistas sobre el tema "Machine learnings"
Li, Tianshu. "Fintech Application in Banking Operations - Application of Machine Learning in Mitigating Bank Derivatives Counterparty Risks". Asian Business Research 4, n.º 3 (8 de octubre de 2019): 1. http://dx.doi.org/10.20849/abr.v4i3.652.
Texto completoMakarov, Vladimir, Christophe Chabbert, Elina Koletou, Fotis Psomopoulos, Natalja Kurbatova, Samuel Ramirez, Chas Nelson, Prashant Natarajan y Bikalpa Neupane. "Good machine learning practices: Learnings from the modern pharmaceutical discovery enterprise". Computers in Biology and Medicine 177 (julio de 2024): 108632. http://dx.doi.org/10.1016/j.compbiomed.2024.108632.
Texto completoKim, 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, n.º 3 (30 de septiembre de 2021): 411–18. http://dx.doi.org/10.24985/kjss.2021.32.3.411.
Texto completoMalik, Sehrish y DoHyeun Kim. "Improved Control Scheduling Based on Learning to Prediction Mechanism for Efficient Machine Maintenance in Smart Factory". Actuators 10, n.º 2 (31 de enero de 2021): 27. http://dx.doi.org/10.3390/act10020027.
Texto completoPREETHAM S, M C CHANDRASHEKHAR y M Z KURIAN. "METHODOLOGY FOR IMPLEMENTATION OF PREDICTION MODEL FOR STUDENTS USING MACHINE LEARNING". international journal of engineering technology and management sciences 7, n.º 3 (2023): 764–66. http://dx.doi.org/10.46647/ijetms.2023.v07i03.116.
Texto completoKurniawan, Robi y Shunsuke Managi. "Forecasting annual energy consumption using machine learnings: Case of Indonesia". IOP Conference Series: Earth and Environmental Science 257 (10 de mayo de 2019): 012032. http://dx.doi.org/10.1088/1755-1315/257/1/012032.
Texto completoSingh, Priyanka, Chakshu Garg, Aman Namdeo, Krishna Mohan Agarwal y 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.
Texto completoDas, Aditi. "Automatic Personality Identification using Machine Learning". International Journal for Research in Applied Science and Engineering Technology 9, n.º VI (30 de junio de 2021): 3528–34. http://dx.doi.org/10.22214/ijraset.2021.35386.
Texto completoMalinda 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, n.º 11 (30 de noviembre de 2023): 56–62. http://dx.doi.org/10.17762/ijritcc.v11i11.9087.
Texto completoSendak, 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, n.º 7 (15 de julio de 2020): e15182. http://dx.doi.org/10.2196/15182.
Texto completoTesis sobre el tema "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.
Texto completoStohr, 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.
Texto completoTebbifakhr, Amirhossein. "Machine Translation For Machines". Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/320504.
Texto completoDinakar, Karthik. "Lensing Machines : representing perspective in machine learning". Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/112523.
Texto completoCataloged 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 y 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.
Texto completoKent, 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.
Texto completoThorén, 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.
Texto completoRomano, 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/.
Texto completoSchneider, C. "Using unsupervised machine learning for fault identification in virtual machines". Thesis, University of St Andrews, 2015. http://hdl.handle.net/10023/7327.
Texto completoSOAVE, 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.
Texto completoNegli 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.
Libros sobre el tema "Machine learnings"
Ertekin, Şeyda. Algorithms for efficient learning systems: Online and active learning approaches. Saarbrücken: VDM Verlag Dr. Müller, 2009.
Buscar texto completoCampbell, Colin. Learning with support vector machines. San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA): Morgan & Claypool, 2011.
Buscar texto completoBoyle, Brandon H. Support vector machines: Data analysis, machine learning, and applications. Hauppauge, N.Y: Nova Science Publishers, 2011.
Buscar texto completoZhou, Zhi-Hua. Machine Learning. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-1967-3.
Texto completoJung, Alexander. Machine Learning. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8193-6.
Texto completoMitchell, Tom M., Jaime G. Carbonell y Ryszard S. Michalski. Machine Learning. Boston, MA: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4613-2279-5.
Texto completoFernandes de Mello, Rodrigo y Moacir Antonelli Ponti. Machine Learning. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94989-5.
Texto completoBell, Jason. Machine Learning. Indianapolis, IN, USA: John Wiley & Sons, Inc, 2014. http://dx.doi.org/10.1002/9781119183464.
Texto completoHuang, Kaizhu, Haiqin Yang, Irwin King y Michael Lyu. Machine Learning. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-79452-3.
Texto completoJebara, Tony. Machine Learning. Boston, MA: Springer US, 2004. http://dx.doi.org/10.1007/978-1-4419-9011-2.
Texto completoCapítulos de libros sobre el tema "Machine learnings"
Heesen, Bernd. "Grundlagen des Machine Learnings mit R". En 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.
Texto completoAugust, Stephanie E. y Audrey Tsaima. "Artificial Intelligence and Machine Learning: An Instructor’s Exoskeleton in the Future of Education". En 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.
Texto completoHan, Haihang, Tianjie Zhang, Qiao Dong, Xueqin Chen y Yangyang Wang. "Pavement roughness level classification based on logistic and decision tree machine learnings". En Green and Intelligent Technologies for Sustainable and Smart Asphalt Pavements, 400–405. London: CRC Press, 2021. http://dx.doi.org/10.1201/9781003251125-63.
Texto completoAwotunde, Joseph Bamidele, Sunday Adeola Ajagbe, Matthew A. Oladipupo, Jimmisayo A. Awokola, Olakunle S. Afolabi, Timothy O. Mathew y Yetunde J. Oguns. "An Improved Machine Learnings Diagnosis Technique for COVID-19 Pandemic Using Chest X-ray Images". En 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.
Texto completoBringsjord, Selmer, Naveen Sundar Govindarajulu, Shreya Banerjee y John Hummel. "Do Machine-Learning Machines Learn?" En 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.
Texto completoDai, Anni. "Co-creation: Space Reconfiguration by Architect and Agent Simulation Based Machine Learning". En Computational Design and Robotic Fabrication, 304–13. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8637-6_27.
Texto completoSödergård, Caj. "Summary of Potential and Exploitation of Big Data and AI in Bioeconomy". En Big Data in Bioeconomy, 417–23. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71069-9_32.
Texto completoPuigbò, Jordi-Ysard, Xerxes D. Arsiwalla y Paul F. M. J. Verschure. "Challenges of Machine Learning for Living Machines". En Biomimetic and Biohybrid Systems, 382–86. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95972-6_41.
Texto completoWehenkel, Louis A. "Machine Learning". En 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.
Texto completoCios, Krzysztof J., Witold Pedrycz y Roman W. Swiniarski. "Machine Learning". En 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.
Texto completoActas de conferencias sobre el tema "Machine learnings"
Gaber, Ayman, Mohamed Mahmoud Zaki, Ahmed Maher Mohamed y Mohamed Abdellatif Beshara. "Cellular Network Power Control Optimization Using Unsupervised Machine Learnings". En 2019 International Conference on Innovative Trends in Computer Engineering (ITCE). IEEE, 2019. http://dx.doi.org/10.1109/itce.2019.8646611.
Texto completoGuajardo, Marco, Ahmed S. Omran y Howard Clark. "Fast model-driven target optimization methods using machine learnings". En Design-Technology Co-optimization XV, editado por Chi-Min Yuan y Ryoung-Han Kim. SPIE, 2021. http://dx.doi.org/10.1117/12.2587122.
Texto completoEshita, Kakeru, Kousei Nishizono, Ryusei Kunitake, Hirohumi Miyazima, Kenichi Arai y Toru Kobayashi. "Surface Roughness Prediction System for Blade Machining Using Machine Learnings". En 2023 IEEE 12th Global Conference on Consumer Electronics (GCCE). IEEE, 2023. http://dx.doi.org/10.1109/gcce59613.2023.10315442.
Texto completoPowney, M., J. Masi, D. Austin, T. Citraningtyas, M. Dyrendahl, B. Alaei, S. Cornelius, F. Dias y P. Emmet. "Legacy Learnings to Future Insight – Characterising CCUS Sites Using Legacy Data with Machine Learning". En First EAGE Workshop on Hydrogen & CCS in LATAM. European Association of Geoscientists & Engineers, 2023. http://dx.doi.org/10.3997/2214-4609.202382004.
Texto completoSato, Keita, Masafumi Chida, Yoshihiro Hayakawa y Nahomi Miyamoto Fujiki. "Automatic Feature Extraction from Wearable Sensor Data by Use of Machine Learnings". En 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.
Texto completoGera, Saksham, Mr Mridul y Kireet Joshi. "Regression Analysis And Future Forecasting Of COVID-19 Using Machine Learnings Algorithm". En 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence). IEEE, 2021. http://dx.doi.org/10.1109/confluence51648.2021.9377065.
Texto completoJusman, Yessi, Muhammad Khoirul Anam, Sartika Puspita y Edwyn Saleh. "Machine Learnings of Dental Caries Images based on Hu Moment Invariants Features". En 2021 International Seminar on Application for Technology of Information and Communication (iSemantic). IEEE, 2021. http://dx.doi.org/10.1109/isemantic52711.2021.9573208.
Texto completoSrivastava, Priyank, Mainak Bandyopadhyay, Shantanu Chakraborty, Samarth Patwardhan y Huy Tran. "Classification of Wireline Formation Testing Responses Using Unsupervised Machine Learning Methods". En Offshore Technology Conference. OTC, 2022. http://dx.doi.org/10.4043/31892-ms.
Texto completoQumsiyeh, Emma, Miar Yousef y Malik Yousef. "ReScore Disease Groups Based on Multiple Machine Learnings Utilizing the Grouping-Scoring-Modeling Approach". En 15th International Conference on Bioinformatics Models, Methods and Algorithms. SCITEPRESS - Science and Technology Publications, 2024. http://dx.doi.org/10.5220/0012379400003657.
Texto completoEmery, David J., Marcelo Guarido, Brian Russell y Daniel Trad. "Machine learnings and lessons learned on improvements to Castagna’s mudrock, Gardner’s density, and Faust’s velocity estimation". En 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.
Texto completoInformes sobre el tema "Machine learnings"
Giannoulakis, Stylianos y Arrigo Beretta. PR-471-18210-R01 Pump Failure and Performance Degradation Prediction. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), septiembre de 2020. http://dx.doi.org/10.55274/r0011801.
Texto completoVesselinov, Velimir Valentinov. Machine Learning. Office of Scientific and Technical Information (OSTI), enero de 2019. http://dx.doi.org/10.2172/1492563.
Texto completoValiant, L. G. Machine Learning. Fort Belvoir, VA: Defense Technical Information Center, enero de 1993. http://dx.doi.org/10.21236/ada283386.
Texto completoChase, Melissa P. Machine Learning. Fort Belvoir, VA: Defense Technical Information Center, abril de 1990. http://dx.doi.org/10.21236/ada223732.
Texto completoKagie, Matthew J. y Park Hays. FORTE Machine Learning. Office of Scientific and Technical Information (OSTI), agosto de 2016. http://dx.doi.org/10.2172/1561828.
Texto completoLin, Youzuo, Shihang Feng y Esteban Rougier. Machine Learning Tutorial. Office of Scientific and Technical Information (OSTI), julio de 2022. http://dx.doi.org/10.2172/1876777.
Texto completoCaplin, Andrew, Daniel Martin y Philip Marx. Modeling Machine Learning. Cambridge, MA: National Bureau of Economic Research, octubre de 2022. http://dx.doi.org/10.3386/w30600.
Texto completoKelly, Bryan y Dacheng Xiu. Financial Machine Learning. Cambridge, MA: National Bureau of Economic Research, julio de 2023. http://dx.doi.org/10.3386/w31502.
Texto completoVassilev, Apostol. Adversarial Machine Learning:. Gaithersburg, MD: National Institute of Standards and Technology, 2024. http://dx.doi.org/10.6028/nist.ai.100-2e2023.
Texto completoJunttila, Jukka, Ville Lämsä, Leonardo Espinosa Leal y Anssi Sillanpää. Feature engineering –based machine learning models for operational state recognition of rotating machines. Peeref, marzo de 2023. http://dx.doi.org/10.54985/peeref.2303p8483224.
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