Literatura académica sobre el tema "Systems for Machine Learning"
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Artículos de revistas sobre el tema "Systems for Machine Learning"
Molino, Piero y Christopher Ré. "Declarative machine learning systems". Communications of the ACM 65, n.º 1 (enero de 2022): 42–49. http://dx.doi.org/10.1145/3475167.
Texto completoMolino, Piero y Christopher Ré. "Declarative Machine Learning Systems". Queue 19, n.º 3 (30 de junio de 2021): 46–76. http://dx.doi.org/10.1145/3475965.3479315.
Texto completoSchneier, Bruce. "Attacking Machine Learning Systems". Computer 53, n.º 5 (mayo de 2020): 78–80. http://dx.doi.org/10.1109/mc.2020.2980761.
Texto completoLitz, Heiner y Milad Hashemi. "Machine Learning for Systems". IEEE Micro 40, n.º 5 (1 de septiembre de 2020): 6–7. http://dx.doi.org/10.1109/mm.2020.3016551.
Texto completoSidorov, Denis, Fang Liu y Yonghui Sun. "Machine Learning for Energy Systems". Energies 13, n.º 18 (10 de septiembre de 2020): 4708. http://dx.doi.org/10.3390/en13184708.
Texto completoEt. al., Mathew Chacko,. "Cyber-Physical Quality Systems in Manufacturing". Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, n.º 2 (11 de abril de 2021): 2006–18. http://dx.doi.org/10.17762/turcomat.v12i2.1805.
Texto completoKelly, Terence. "Steampunk Machine Learning". Queue 19, n.º 6 (31 de diciembre de 2021): 5–17. http://dx.doi.org/10.1145/3511543.
Texto completoAmbore, Anil Kumar, T. Sri Sai Charan, U. Rohit Reddy, T. Samara Simha Reddy y Tarun G. "Flood Prediction using Machine Learning". International Journal for Research in Applied Science and Engineering Technology 11, n.º 5 (31 de mayo de 2023): 363–67. http://dx.doi.org/10.22214/ijraset.2023.51528.
Texto completoAnggi Rachmawati y Yossaepurrohman. "Analysis of Machine Learning Systems for Cyber Physical Systems". International Transactions on Education Technology (ITEE) 1, n.º 1 (24 de noviembre de 2022): 1–9. http://dx.doi.org/10.34306/itee.v1i1.170.
Texto completoAnggi Rachmawati y Yossaepurrohman. "Analysis of Machine Learning Systems for Cyber Physical Systems". International Transactions on Education Technology (ITEE) 1, n.º 1 (24 de noviembre de 2022): 1–9. http://dx.doi.org/10.33050/itee.v1i1.170.
Texto completoTesis sobre el tema "Systems for Machine Learning"
Shukla, Ritesh. "Machine learning ecosystem : implications for business strategy centered on machine learning". Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/107342.
Texto completoCataloged from PDF version of thesis.
Includes bibliographical references (pages 48-50).
As interest for adopting machine learning as a core component of a business strategy increases, business owners face the challenge of integrating an uncertain and rapidly evolving technology into their organization, and depending on this for the success of their strategy. The field of Machine learning has a rich set of literature for modeling of technical systems that implement machine learning. This thesis attempts to connect the literature for business and technology and for evolution and adoption of technology to the emergent properties of machine learning systems. This thesis provides high-level levers and frameworks to better prepare business owners to adopt machine learning to satisfy their strategic goals.
by Ritesh Shukla.
S.M. in Engineering and Management
Andersson, Viktor. "Machine Learning in Logistics: Machine Learning Algorithms : Data Preprocessing and Machine Learning Algorithms". Thesis, Luleå tekniska universitet, Datavetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-64721.
Texto completoData Ductus är ett svenskt IT-konsultbolag, deras kundbas sträcker sig från små startups till stora redan etablerade företag. Företaget har stadigt växt sedan 80-talet och har etablerat kontor både i Sverige och i USA. Med hjälp av maskininlärning kommer detta projket att presentera en möjlig lösning på de fel som kan uppstå inom logistikverksamheten, orsakade av den mänskliga faktorn.Ett sätt att förbehandla data innan den tillämpas på en maskininlärning algoritm, liksom ett par algoritmer för användning kommer att presenteras.
Swere, Erick A. R. "Machine learning in embedded systems". Thesis, Loughborough University, 2008. https://dspace.lboro.ac.uk/2134/4969.
Texto completoVerleyen, Wim. "Machine learning for systems pathology". Thesis, University of St Andrews, 2013. http://hdl.handle.net/10023/4512.
Texto completoRoderus, 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 completoJohansson, Richard. "Machine learning på tidsseriedataset : En utvärdering av modeller i Azure Machine Learning Studio". Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-71223.
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 completoMichailidis, Marios. "Investigating machine learning methods in recommender systems". Thesis, University College London (University of London), 2017. http://discovery.ucl.ac.uk/10031000/.
Texto completoIlyas, Andrew. "On practical robustness of machine learning systems". Thesis, Massachusetts Institute of Technology, 2018. https://hdl.handle.net/1721.1/122911.
Texto completoThesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 71-79).
We consider the importance of robustness in evaluating machine learning systems, an in particular systems involving deep learning. We consider these systems' vulnerability to adversarial examples--subtle, crafted perturbations to inputs which induce large change in output. We show that these adversarial examples are not only theoretical concern, by desigining the first 3D adversarial objects, and by demonstrating that these examples can be constructed even when malicious actors have little power. We suggest a potential avenue for building robust deep learning models by leveraging generative models.
by Andrew Ilyas.
M. Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
ROSA, BRUSIN ANN MARGARETH. "Machine Learning Applications to Optical Communication Systems". Doctoral thesis, Politecnico di Torino, 2022. http://hdl.handle.net/11583/2967019.
Texto completoLibros sobre el tema "Systems for Machine Learning"
Chen, Joy Iong-Zong, Haoxiang Wang, Ke-Lin Du y V. Suma, eds. Machine Learning and Autonomous Systems. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7996-4.
Texto completoAo, Sio-Iong, Burghard Rieger y Mahyar A. Amouzegar, eds. Machine Learning and Systems Engineering. Dordrecht: Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-9419-3.
Texto completoB, Rieger Burghard, Amouzegar Mahyar A y SpringerLink (Online service), eds. Machine Learning and Systems Engineering. Dordrecht: Springer Science+Business Media B.V., 2011.
Buscar texto completoNandan Mohanty, Sachi, Vicente Garcia Diaz y G. A. E. Satish Kumar, eds. Intelligent Systems and Machine Learning. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-35078-8.
Texto completoNandan Mohanty, Sachi, Vicente Garcia Diaz y G. A. E. Satish Kumar, eds. Intelligent Systems and Machine Learning. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-35081-8.
Texto completoChandran, C. Karthik, M. Rajalakshmi, Sachi Nandan Mohanty y Subrata Chowdhury. Machine Learning for Healthcare Systems. New York: River Publishers, 2023. http://dx.doi.org/10.1201/9781003438816.
Texto completoErtekin, Şeyda. Algorithms for efficient learning systems: Online and active learning approaches. Saarbrücken: VDM Verlag Dr. Müller, 2009.
Buscar texto completoBeyerer, Jürgen, Christian Kühnert y Oliver Niggemann, eds. Machine Learning for Cyber Physical Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2019. http://dx.doi.org/10.1007/978-3-662-58485-9.
Texto completoBeyerer, Jürgen, Alexander Maier y Oliver Niggemann, eds. Machine Learning for Cyber Physical Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2021. http://dx.doi.org/10.1007/978-3-662-62746-4.
Texto completoNiggemann, Oliver y Jürgen Beyerer, eds. Machine Learning for Cyber Physical Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-48838-6.
Texto completoCapítulos de libros sobre el tema "Systems for Machine Learning"
Zielesny, Achim. "Machine Learning". En Intelligent Systems Reference Library, 221–380. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21280-2_4.
Texto completoGrosan, Crina y Ajith Abraham. "Machine Learning". En Intelligent Systems Reference Library, 261–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21004-4_10.
Texto completoZielesny, Achim. "Machine Learning". En Intelligent Systems Reference Library, 229–406. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32545-3_4.
Texto completoSubramanian, Devika y Trevor A. Cohen. "Machine Learning Systems". En Cognitive Informatics in Biomedicine and Healthcare, 135–211. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09108-7_6.
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 completoSotiropoulos, Dionisios N. y George A. Tsihrintzis. "Artificial Immune Systems". En Machine Learning Paradigms, 159–235. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47194-5_7.
Texto completoHulten, Geoff. "Machine Learning Intelligence". En Building Intelligent Systems, 245–61. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3432-7_20.
Texto completoKulkarni, Parag. "Systemic Machine Learning". En Intelligent Systems Reference Library, 49–58. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55312-2_3.
Texto completoKulkarni, Parag. "Creative Machine Learning". En Intelligent Systems Reference Library, 87–118. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55312-2_5.
Texto completoGalakatos, Alex, Andrew Crotty y Tim Kraska. "Distributed Machine Learning". En Encyclopedia of Database Systems, 1–6. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4899-7993-3_80647-1.
Texto completoActas de conferencias sobre el tema "Systems for Machine Learning"
Chu, Albert B., Du T. Nguyen, Alan D. Kaplan y Brian Giera. "Image classification and control of microfluidic systems". En Applications of Machine Learning, editado por Michael E. Zelinski, Tarek M. Taha, Jonathan Howe, Abdul A. Awwal y Khan M. Iftekharuddin. SPIE, 2019. http://dx.doi.org/10.1117/12.2530416.
Texto completo"Machine Learning". En 2019 International Conference on Systems, Signals and Image Processing (IWSSIP). IEEE, 2019. http://dx.doi.org/10.1109/iwssip.2019.8787334.
Texto completo"Machine Learning". En 2022 29th International Conference on Systems, Signals and Image Processing (IWSSIP). IEEE, 2022. http://dx.doi.org/10.1109/iwssip55020.2022.9854395.
Texto completoIvanov, Tonislav, Ayush Kumar, Denis Sharoukhov, Francis A. Ortega y Matthew Putman. "DeepFocus: A deep learning model for focusing microscope systems". En Applications of Machine Learning 2020, editado por Michael E. Zelinski, Tarek M. Taha, Jonathan Howe, Abdul A. Awwal y Khan M. Iftekharuddin. SPIE, 2020. http://dx.doi.org/10.1117/12.2568990.
Texto completoAxtell, Travis, Lucas A. Overbey y Lisa Woerner. "Machine learning in complex systems". En Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR IX, editado por Tien Pham, Michael A. Kolodny y Dietrich M. Wiegmann. SPIE, 2018. http://dx.doi.org/10.1117/12.2309547.
Texto completoZhang, Jeff Jun, Kang Liu, Faiq Khalid, Muhammad Abdullah Hanif, Semeen Rehman, Theocharis Theocharides, Alessandro Artussi, Muhammad Shafique y Siddharth Garg. "Building Robust Machine Learning Systems". En DAC '19: The 56th Annual Design Automation Conference 2019. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3316781.3323472.
Texto completoMartin, Hugo, Juliana Alves Pereira, Mathieu Acher y Paul Temple. "Machine Learning and Configurable Systems". En SPLC 2019: 23rd International Systems and Software Product Line Conference. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3336294.3342383.
Texto completoFanca, Alexandra, Adela Puscasiu, Dan-Ioan Gota y Honoriu Valean. "Recommendation Systems with Machine Learning". En 2020 21th International Carpathian Control Conference (ICCC). IEEE, 2020. http://dx.doi.org/10.1109/iccc49264.2020.9257290.
Texto completoEL MESTARI, Soumia Zohra. "Privacy Preserving Machine Learning Systems". En AIES '22: AAAI/ACM Conference on AI, Ethics, and Society. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3514094.3539530.
Texto completoPereira, Juliana Alves, Hugo Martin, Paul Temple y Mathieu Acher. "Machine learning and configurable systems". En SPLC '20: 24th ACM International Systems and Software Product Line Conference. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3382025.3414976.
Texto completoInformes sobre el tema "Systems for Machine Learning"
Rouet-Leduc, Bertrand Philippe Gerard. Fault systems monitoring using machine learning. Office of Scientific and Technical Information (OSTI), septiembre de 2019. http://dx.doi.org/10.2172/1569601.
Texto completoCary, Dakota y Daniel Cebul. Destructive Cyber Operations and Machine Learning. Center for Security and Emerging Technology, noviembre de 2020. http://dx.doi.org/10.51593/2020ca003.
Texto completoGordon, Diane F. y William M. Spears. Machine Learning Systems: Part 1. Concept Learning from Examples with AQ15 and Related Systems. Fort Belvoir, VA: Defense Technical Information Center, septiembre de 1991. http://dx.doi.org/10.21236/ada242472.
Texto completoMusser, Micah. Adversarial Machine Learning and Cybersecurity. Center for Security and Emerging Technology, abril de 2023. http://dx.doi.org/10.51593/2022ca003.
Texto completoValasek, John y Suman Chakravorty. Machine Learning Control For Highly Reconfigurable High-Order Systems. Fort Belvoir, VA: Defense Technical Information Center, enero de 2015. http://dx.doi.org/10.21236/ada614672.
Texto completoStone, Peter y Manuela Veloso. Multiagent Systems: A Survey from a Machine Learning Perspective. Fort Belvoir, VA: Defense Technical Information Center, diciembre de 1997. http://dx.doi.org/10.21236/ada333248.
Texto completoAnania, Mark, George Corbin, Matthew Kovacs, Kevin Nelson y Jeremy Tobias. Thutmose - Investigation of Machine Learning-Based Intrusion Detection Systems. Fort Belvoir, VA: Defense Technical Information Center, junio de 2016. http://dx.doi.org/10.21236/ad1011870.
Texto completoNickerson, Jeffrey, Kalle Lyytinen y John L. King. Automated Vehicles: A Human/Machine Co-learning Perspective. SAE International, abril de 2022. http://dx.doi.org/10.4271/epr2022009.
Texto completoSzunyogh, Istvan, Edward Ott y Brian Hunt. Machine-Learning-Assisted Hybrid Earth System Modelling. Office of Scientific and Technical Information (OSTI), abril de 2021. http://dx.doi.org/10.2172/1769745.
Texto completoRudner, Tim y Helen Toner. Key Concepts in AI Safety: Interpretability in Machine Learning. Center for Security and Emerging Technology, marzo de 2021. http://dx.doi.org/10.51593/20190042.
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