Academic literature on the topic 'Adaptive machine learning'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Adaptive machine learning.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Journal articles on the topic "Adaptive machine learning"
Wilson, Craig, Yuheng Bu, and Venugopal V. Veeravalli. "Adaptive sequential machine learning." Sequential Analysis 38, no. 4 (October 2, 2019): 545–68. http://dx.doi.org/10.1080/07474946.2019.1686889.
Full textWang, Gai-Ge, Mei Lu, Yong-Quan Dong, and Xiang-Jun Zhao. "Self-adaptive extreme learning machine." Neural Computing and Applications 27, no. 2 (March 21, 2015): 291–303. http://dx.doi.org/10.1007/s00521-015-1874-3.
Full textShoureshi, R., D. Swedes, and R. Evans. "Learning Control for Autonomous Machines." Robotica 9, no. 2 (April 1991): 165–70. http://dx.doi.org/10.1017/s0263574700010201.
Full textMundhe, Shivani. "Image Segmentation using Adaptive Machine Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. 5 (May 31, 2021): 948–50. http://dx.doi.org/10.22214/ijraset.2021.34383.
Full textHie, Brian L., and Kevin K. Yang. "Adaptive machine learning for protein engineering." Current Opinion in Structural Biology 72 (February 2022): 145–52. http://dx.doi.org/10.1016/j.sbi.2021.11.002.
Full textMajumdar, Somshubra, Ishaan Jain, and Kunal Kukreja. "AdaSort: Adaptive Sorting using Machine Learning." International Journal of Computer Applications 145, no. 12 (July 15, 2016): 12–17. http://dx.doi.org/10.5120/ijca2016910726.
Full textStange, Renata Luiza, and Joao Jose. "Applying Adaptive Technology in Machine Learning." IEEE Latin America Transactions 12, no. 7 (October 2014): 1298–306. http://dx.doi.org/10.1109/tla.2014.6948866.
Full textCao, Jiuwen, Zhiping Lin, and Guang-Bin Huang. "Self-Adaptive Evolutionary Extreme Learning Machine." Neural Processing Letters 36, no. 3 (July 18, 2012): 285–305. http://dx.doi.org/10.1007/s11063-012-9236-y.
Full textHasan, Sajib. "Adaptive Fitts for Adaptive Interface." AIUB Journal of Science and Engineering (AJSE) 17, no. 2 (July 31, 2018): 51–58. http://dx.doi.org/10.53799/ajse.v17i2.9.
Full textChabaud, Ulysse, Damian Markham, and Adel Sohbi. "Quantum machine learning with adaptive linear optics." Quantum 5 (July 5, 2021): 496. http://dx.doi.org/10.22331/q-2021-07-05-496.
Full textDissertations / Theses on the topic "Adaptive machine learning"
Jelfs, Beth. "Collaborative adaptive filtering for machine learning." Thesis, Imperial College London, 2009. http://hdl.handle.net/10044/1/5598.
Full textMiles, Jonathan David. "Machine Learning for Adaptive Computer Game Opponents." The University of Waikato, 2009. http://hdl.handle.net/10289/2779.
Full textLong, Shun. "Adaptive Java optimisation using machine learning techniques." Thesis, University of Edinburgh, 2004. http://hdl.handle.net/1842/567.
Full textAr, Rosyid Harits. "Adaptive serious educational games using machine learning." Thesis, University of Manchester, 2018. https://www.research.manchester.ac.uk/portal/en/theses/adaptive-serious-educational-games-using-machine-learning(b5f5024b-c7fd-4660-997c-9fd22e140a8f).html.
Full textDal, Pozzolo Andrea. "Adaptive Machine Learning for Credit Card Fraud Detection." Doctoral thesis, Universite Libre de Bruxelles, 2015. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/221654.
Full textDoctorat en Sciences
info:eu-repo/semantics/nonPublished
Clement, Benjamin. "Adaptive Personalization of Pedagogical Sequences using Machine Learning." Thesis, Bordeaux, 2018. http://www.theses.fr/2018BORD0373/document.
Full textCan computers teach people? To answer this question, Intelligent Tutoring Systems are a rapidly expanding field of research among the Information and Communication Technologies for the Education community. This subject brings together different issues and researchers from various fields, such as psychology, didactics, neurosciences and, particularly, machine learning. Digital technologies are becoming more and more a part of everyday life with the development of tablets and smartphones. It seems natural to consider using these technologies for educational purposes. This raises several questions, such as how to make user interfaces accessible to everyone, how to make educational content motivating and how to customize it to individual learners. In this PhD, we developed methods, grouped in the aptly-named HMABITS framework, to adapt pedagogical activity sequences based on learners' performances and preferences to maximize their learning speed and motivation. These methods use computational models of intrinsic motivation and curiosity-driven learning to identify the activities providing the highest learning progress and use Multi-Armed Bandit algorithms to manage the exploration/exploitation trade-off inside the activity space. Activities of optimal interest are thus privileged with the target to keep the learner in a state of Flow or in his or her Zone of Proximal Development. Moreover, some of our methods allow the student to make choices about contextual features or pedagogical content, which is a vector of self-determination and motivation. To evaluate the effectiveness and relevance of our algorithms, we carried out several types of experiments. We first evaluated these methods with numerical simulations before applying them to real teaching conditions. To do this, we developed multiple models of learners, since a single model never exactly replicates the behavior of a real learner. The simulation results show the HMABITS framework achieves comparable, and in some cases better, learning results than an optimal solution or an expert sequence. We then developed our own pedagogical scenario and serious game to test our algorithms in classrooms with real students. We developed a game on the theme of number decomposition, through the manipulation of money, for children aged 6 to 8. We then worked with the educational institutions and several schools in the Bordeaux school district. Overall, about 1000 students participated in trial lessons using the tablet application. The results of the real-world studies show that the HMABITS framework allows the students to do more diverse and difficult activities, to achieve better learning and to be more motivated than with an Expert Sequence. The results show that this effect is even greater when the students have the possibility to make choices
Wiens, Jenna Marleau. "Machine learning for patient-adaptive ectopic beat classification." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/60823.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (p. 83-85).
Physicians require automated techniques to accurately analyze the vast amount of physiological data collected by continuous monitoring devices. In this thesis, we consider one analysis task in particular, the classification of heartbeats from electrocardiographic recordings (ECG). This problem is made challenging by the inter-patient differences present in ECG morphology and timing characteristics. Supervised classifiers trained on a collection of patients can have unpredictable results when applied to a new patient. To reduce the effect of inter-patient differences, researchers have suggested training patient-adative classifiers by training on labeled data from the test patient. However, patient-adaptive classifiers have not been integrated in practice because they require an impractical amount of patient-specific expert knowledge. We present two approaches based on machine learning for building accurate patientadaptive beat classifiers that use little or no patient-specific expert knowledge. First, we present a method to transfer and adapt knowledge from a collection of patients to a test-patient. This first approach, based on transductive transfer learning, requires no patient-specific labeled data, only labeled data from other patients. Second, we consider the scenario where patient-specific expert knowledge is available, but comes at a high cost. We present a novel algorithm for SVM active learning. By intelligently selecting the training set we show how one can build highly accurate patient-adaptive classifiers using only a small number of cardiologist supplied labels. Our results show the gains in performance possible when using patient-adaptive classifiers in place of global classifiers. Furthermore, the effectiveness of our techniques, which use little or no patient-specific expert knowledge, suggest that it is also practical to use patient-adaptive techniques in a clinical setting.
by Jenna Marleau Wiens.
S.M.
Drolia, Utsav. "Adaptive Distributed Caching for Scalable Machine Learning Services." Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/1004.
Full textYin, Wenjie. "Machine Learning for Adaptive Cruise Control Target Selection." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-264918.
Full textFordon kommer att vara mer komplexa, säkra och intelligenta i framtiden. Till exempel, med stöd av det avancerade förarassistanssystemet (ADAS) kan föraren och passagerarnas säkerhet och komfort förbättras avsevärt. Detta examensarbete föreslår datastyrda lösningar för målval för adaptivt fartreglering (ACC) för att välja ett av föregående fordon som det primära målet. Valet liknar det som människor gör. Arbetet genomfördes i samarbete med Scania CV AB. Ett delat nätverk och ett gemensamt LSTM-nätverk användes för att välja det primära målet. Dessutom har en ny maskinbaserad målvalsmodell (jämförelse-målmodell) utformats, vilken kan överväga alla närliggande fordon tillsammans genom att jämföra fordon. Ett jämför-mål-nätverk och ett jämförbart mål XGBoost utvecklas baserat på jämförelsemodellen. Totalt användes fyra olika maskininlärningsmetoder för att välja det primara målet för ACC, inklusive ett delat nätverk, ett gemensamt LSTM-nätverk, ett jämförelsemål-nätverk och en jämförbar XGBoost-modell. Dessa meto- der jämfördes och analyserades. Finjustering antogs för att motverka dataobalansproblemet för sällsynta situationer. Jämförelse-målet XGBoost kan uppnå 94.85% noggrannhet på testuppsättningen.
Grundtman, Per. "Adaptive Learning." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-61648.
Full textBooks on the topic "Adaptive machine learning"
Chatterjee, Chanchal. Adaptive Machine Learning Algorithms with Python. Berkeley, CA: Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8017-1.
Full textLopes, Noel, and Bernardete Ribeiro. Machine Learning for Adaptive Many-Core Machines - A Practical Approach. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-06938-8.
Full textBhanu, Bir. Genetic learning for adaptive image segmentation. Boston: Kluwer Academic Publishers, 1994.
Find full textHe, Haibo. Self-adaptive systems for machine intelligence. Hoboken, N.J: Wiley-Interscience, 2011.
Find full textWhiteson, Shimon. Adaptive representations for reinforcement learning. Berlin: Springer Verlag, 2010.
Find full textGori͡achkin, O. V. Metody slepoĭ obrabotki signalov i ikh prilozhenii͡a v sistemakh radiotekhniki i svi͡azi. Moskva: Radio i svi͡azʹ, 2003.
Find full textWei, Chia-Hung. Machine learning techniques for adaptive multimedia retrieval: Technologies, applications, and perspectives. Hershey, PA: Information Science Reference, 2011.
Find full text1938-, Mendel Jerry M., ed. A Prelude to neural networks: Adaptive and learning systems. Englewood Cliffs, N.J: PTR Prentice Hall, 1994.
Find full text1975-, Butz Martin V., and International Conference on Simulation of Adaptive Behavior (9th : 2006 : Rome, Italy), eds. Anticipatory behavior in adaptive learning systems: From brains to individual and social behavior. Berlin: Springer, 2007.
Find full textCichocki, Andrzej. Adaptive blind signal and image processing: Learning algorithms and applications. Chichester: J. Wiley, 2002.
Find full textBook chapters on the topic "Adaptive machine learning"
Paluszek, Michael, and Stephanie Thomas. "Adaptive Control." In MATLAB Machine Learning, 207–68. Berkeley, CA: Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-2250-8_11.
Full textPaluszek, Michael, and Stephanie Thomas. "Adaptive Control." In MATLAB Machine Learning Recipes, 109–33. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-3916-2_5.
Full textKakas, Antonis C., David Cohn, Sanjoy Dasgupta, Andrew G. Barto, Gail A. Carpenter, Stephen Grossberg, Geoffrey I. Webb, et al. "Adaptive System." In Encyclopedia of Machine Learning, 35. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_12.
Full textGolden, Richard M. "Adaptive Learning Algorithm Convergence." In Statistical Machine Learning, 325–58. First edition. j Boca Raton, FL : CRC Press, 2020. j Includes bibliographical references and index.: Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9781351051507-12.
Full textvan Someren, M., and S. ten Hagen. "Machine Learning and Reinforcement Learning." In Do Smart Adaptive Systems Exist?, 81–104. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/3-540-32374-0_5.
Full textKakas, Antonis C., David Cohn, Sanjoy Dasgupta, Andrew G. Barto, Gail A. Carpenter, Stephen Grossberg, Geoffrey I. Webb, et al. "Adaptive Resonance Theory." In Encyclopedia of Machine Learning, 22–35. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_11.
Full textShultz, Thomas R., Scott E. Fahlman, Susan Craw, Periklis Andritsos, Panayiotis Tsaparas, Ricardo Silva, Chris Drummond, et al. "Complex Adaptive System." In Encyclopedia of Machine Learning, 194. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_147.
Full textKakas, Antonis C., David Cohn, Sanjoy Dasgupta, Andrew G. Barto, Gail A. Carpenter, Stephen Grossberg, Geoffrey I. Webb, et al. "Adaptive Control Processes." In Encyclopedia of Machine Learning, 19. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_9.
Full textShultz, Thomas R., Scott E. Fahlman, Susan Craw, Periklis Andritsos, Panayiotis Tsaparas, Ricardo Silva, Chris Drummond, et al. "Complexity in Adaptive Systems." In Encyclopedia of Machine Learning, 194–98. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_148.
Full textKakas, Antonis C., David Cohn, Sanjoy Dasgupta, Andrew G. Barto, Gail A. Carpenter, Stephen Grossberg, Geoffrey I. Webb, et al. "Adaptive Real-Time Dynamic Programming." In Encyclopedia of Machine Learning, 19–22. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_10.
Full textConference papers on the topic "Adaptive machine learning"
Wong, Alison, Barnaby R. M. Norris, Vincent Deo, Olivier Guyon, Peter G. Tuthill, Julien Lozi, Sébastien Vievard, and Kyohoon Ahn. "Machine learning for wavefront sensing." In Adaptive Optics Systems VIII, edited by Dirk Schmidt, Laura Schreiber, and Elise Vernet. SPIE, 2022. http://dx.doi.org/10.1117/12.2628869.
Full textRossi, Fabio, Cédric Plantet, Anne-Laure Lucie Ceffot, Guido Agapito, Enrico Pinna, and Simone Esposito. "Machine learning techniques for piston sensing." In Adaptive Optics Systems VIII, edited by Dirk Schmidt, Laura Schreiber, and Elise Vernet. SPIE, 2022. http://dx.doi.org/10.1117/12.2629983.
Full textMehanna, Ryan. "Resilient Structures Through Machine Learning And Evolution." In ACADIA 2013: Adaptive Architecture. ACADIA, 2013. http://dx.doi.org/10.52842/conf.acadia.2013.319.
Full textOboko, Robert O., Elizaphan M. Maina, Peter W. Waiganjo, Elijah I. Omwenga, and Ruth D. Wario. "Designing adaptive learning support through machine learning techniques." In 2016 IST-Africa Week Conference. IEEE, 2016. http://dx.doi.org/10.1109/istafrica.2016.7530676.
Full textHentschel, Alexander, and Barry C. Sanders. "Machine Learning for Adaptive Quantum Measurement." In 2010 Seventh International Conference on Information Technology: New Generations. IEEE, 2010. http://dx.doi.org/10.1109/itng.2010.109.
Full textBrockmann, W. "Online Machine Learning For Adaptive Control." In IEEE International Workshop on Emerging Technologies and Factory Automation,. IEEE, 1992. http://dx.doi.org/10.1109/etfa.1992.683251.
Full textFrosio, Iuri, Karen Egiazarian, and Kari Pulli. "Machine learning for adaptive bilateral filtering." In IS&T/SPIE Electronic Imaging, edited by Karen O. Egiazarian, Sos S. Agaian, and Atanas P. Gotchev. SPIE, 2015. http://dx.doi.org/10.1117/12.2077733.
Full textTurchi, Alessio, Elena Masciadri, and Luca Fini. "Optical turbulence forecast over short timescales using machine learning techniques." In Adaptive Optics Systems VIII, edited by Dirk Schmidt, Laura Schreiber, and Elise Vernet. SPIE, 2022. http://dx.doi.org/10.1117/12.2629501.
Full textZhou, Tao, Yan-Ning Zhang, He-Jing Yuan, and Hui-Ling Lu. "Rough k-means Cluster with Adaptive Parameters." In Sixth International Conference on Machine Learning Cybernetics. IEEE, 2007. http://dx.doi.org/10.1109/icmlc.2007.4370674.
Full textLee, C. S., and A. Elgammal. "Style Adaptive Bayesian Tracking Using Explicit Manifold Learning." In British Machine Vision Conference 2005. British Machine Vision Association, 2005. http://dx.doi.org/10.5244/c.19.55.
Full textReports on the topic "Adaptive machine learning"
Gur, Ilan. Real-Time Adaptive Machine Learning Platform. Office of Scientific and Technical Information (OSTI), March 2020. http://dx.doi.org/10.2172/1607789.
Full textXu, Yuesheng. Adaptive Kernel Based Machine Learning Methods. Fort Belvoir, VA: Defense Technical Information Center, October 2012. http://dx.doi.org/10.21236/ada588768.
Full textMueller, Juliane, Charuleka Varadharajan, Erica Siirila-Woodburn, and Charles Koven. Machine Learning for Adaptive Model Refinement to Bridge Scales. Office of Scientific and Technical Information (OSTI), April 2021. http://dx.doi.org/10.2172/1769741.
Full textComstock, Jennifer, Joseph Hardin, and Zhe Feng. Framework for an adaptive integrated observation system using a hierarchy of machine learning approaches. Office of Scientific and Technical Information (OSTI), April 2021. http://dx.doi.org/10.2172/1769712.
Full textHovakimyan, Naira, Hunmin Kim, Wenbin Wan, and Chuyuan Tao. Safe Operation of Connected Vehicles in Complex and Unforeseen Environments. Illinois Center for Transportation, August 2022. http://dx.doi.org/10.36501/0197-9191/22-016.
Full textScheinker, Alexander, and Reeju Pokharel. Adaptive Machine Learning for Bragg Coherent Diffraction Imaging (BCDI) of 3D Electron Density Maps with Application to La2-xBaxCuO4 (LBCO) High Temperature Superconductor Studies. Office of Scientific and Technical Information (OSTI), October 2020. http://dx.doi.org/10.2172/1671079.
Full textBerry, Jonathan, Anand Ganti, Kenneth Goss, Carolyn Mayer, Uzoma Onunkwo, Cynthia Phillips, Jared Saia, and Timothy Shead. Adapting Secure MultiParty Computation to Support Machine Learning in Radio Frequency Sensor Networks. Office of Scientific and Technical Information (OSTI), January 2022. http://dx.doi.org/10.2172/1842271.
Full text