Literatura académica sobre el tema "MACHINE LEARNING TOOL"
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Artículos de revistas sobre el tema "MACHINE LEARNING TOOL"
E, Prabhakar, Suresh Kumar V.S, Nandagopal S y Dhivyaa C.R. "Mining Better Advertisement Tool for Government Schemes Using Machine Learning". International Journal of Psychosocial Rehabilitation 23, n.º 4 (20 de diciembre de 2019): 1122–35. http://dx.doi.org/10.37200/ijpr/v23i4/pr190439.
Texto completoMonostori, László. "Learning procedures in machine tool monitoring". Computers in Industry 7, n.º 1 (febrero de 1986): 53–64. http://dx.doi.org/10.1016/0166-3615(86)90009-6.
Texto completoKokar, Mieczyslaw M., Jerzy Letkowski y Thomas F. Callahan. "Learning to monitor a machine tool". Journal of Intelligent & Robotic Systems 12, n.º 2 (junio de 1995): 103–25. http://dx.doi.org/10.1007/bf01258381.
Texto completoGittler, Thomas, Stephan Scholze, Alisa Rupenyan y Konrad Wegener. "Machine Tool Component Health Identification with Unsupervised Learning". Journal of Manufacturing and Materials Processing 4, n.º 3 (2 de septiembre de 2020): 86. http://dx.doi.org/10.3390/jmmp4030086.
Texto completoBaltruschat, Marcel y Paul Czodrowski. "Machine learning meets pKa". F1000Research 9 (13 de febrero de 2020): 113. http://dx.doi.org/10.12688/f1000research.22090.1.
Texto completoBaltruschat, Marcel y Paul Czodrowski. "Machine learning meets pKa". F1000Research 9 (27 de abril de 2020): 113. http://dx.doi.org/10.12688/f1000research.22090.2.
Texto completoFinlay, Janet. "Machine learning: A tool to support usability?" Applied Artificial Intelligence 11, n.º 7-8 (octubre de 1997): 633–51. http://dx.doi.org/10.1080/088395197117966.
Texto completoCaté, Antoine, Lorenzo Perozzi, Erwan Gloaguen y Martin Blouin. "Machine learning as a tool for geologists". Leading Edge 36, n.º 3 (marzo de 2017): 215–19. http://dx.doi.org/10.1190/tle36030215.1.
Texto completoWhitehall, B. L., S. C. Y. Lu y R. E. Stepp. "CAQ: A machine learning tool for engineering". Artificial Intelligence in Engineering 5, n.º 4 (octubre de 1990): 189–98. http://dx.doi.org/10.1016/0954-1810(90)90020-5.
Texto completoWang, Zhi‐Lei, Toshio Ogawa y Yoshitaka Adachi. "A Machine Learning Tool for Materials Informatics". Advanced Theory and Simulations 3, n.º 1 (18 de noviembre de 2019): 1900177. http://dx.doi.org/10.1002/adts.201900177.
Texto completoTesis sobre el tema "MACHINE LEARNING TOOL"
Wusteman, Judith. "EBKAT : an explanation-based knowledge acquisition tool". Thesis, University of Exeter, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.280682.
Texto completoCooper, Clayton Alan. "Milling Tool Condition Monitoring Using Acoustic Signals and Machine Learning". Case Western Reserve University School of Graduate Studies / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=case1575539872711423.
Texto completoBUBACK, SILVANO NOGUEIRA. "USING MACHINE LEARNING TO BUILD A TOOL THAT HELPS COMMENTS MODERATION". PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2011. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=19232@1.
Texto completoOne of the main changes brought by Web 2.0 is the increase of user participation in content generation mainly in social networks and comments in news and service sites. These comments are valuable to the sites because they bring feedback and motivate other people to participate and to spread the content. On the other hand these comments also bring some kind of abuse as bad words and spam. While for some sites their own community moderation is enough, for others this impropriate content may compromise its content. In order to help theses sites, a tool that uses machine learning techniques was built to mediate comments. As a test to compare results, two datasets captured from Globo.com were used: the first one with 657.405 comments posted through its site and the second with 451.209 messages captured from Twitter. Our experiments show that best result is achieved when comment learning is done according to the subject that is being commented.
Binsaeid, Sultan Hassan. "Multisensor Fusion for Intelligent Tool Condition Monitoring (TCM) in End Milling Through Pattern Classification and Multiclass Machine Learning". Scholarly Repository, 2007. http://scholarlyrepository.miami.edu/oa_dissertations/7.
Texto completoGert, Oskar. "Using Machine Learning as a Tool to Improve Train Wheel Overhaul Efficiency". Thesis, Linköpings universitet, Medie- och Informationsteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-171121.
Texto completoEDIN, ANTON y MARIAM QORBANZADA. "E-Learning as a tool to support the integration of machine learning in product development processes". Thesis, KTH, Skolan för industriell teknik och management (ITM), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279757.
Texto completoDetta forskningsarbete fokuserar på tillämpningar av elektroniska utlärningsmetoder som alternativ till lokala lektioner vid integrering av maskininlärning i produktutvecklingsprocessen. Framförallt är syftet att undersöka om det går att använda elektroniska utlärningsmetoder för att göra maskininlärning mer tillgänglig i produktutvecklingsprocessen. Detta ämne presenterar sig som intressant då en djupare förståelse kring detta banar väg för att effektivisera lärande på distans samt skalbarheten av kunskapsspridning. För att uppnå detta bads två grupper av anställda hos samma företagsgrupp, men tillhörande olika geografiska områden att ta del i ett upplägg av lektioner som författarna hade tagit fram. En grupp fick ta del av materialet genom seminarier, medan den andra bjöds in till att delta i en serie tele-lektioner. När båda deltagargrupper hade genomgått lektionerna fick några deltagare förfrågningar om att bli intervjuade. Några av deltagarnas direkta chefer och projektledare intervjuades även för att kunna jämföra deltagarnas åsikter med icke-deltagande intressenter. En kombination av en kvalitativ teoretisk analys tillsammans med svaren från intervjuerna användes som bas för de presenterade resultaten. Svarande indikerade att de föredrog träningarna som hölls på plats, men vidare kodning av intervjusvaren visade på undervisningsmetoden inte hade större påverkningar på deltagarnas förmåga att ta till sig materialet. Trots att resultatet pekar på att elektroniskt lärande är en teknik med många fördelar verkar det som att brister i teknikens förmåga att integrera mänsklig interaktion hindrar den från att nå sitt fulla potential och därigenom även hindrar dess integration i produktutvecklingsprocessen.
Bheemireddy, Shruthi. "MACHINE LEARNING-BASED ONTOLOGY MAPPING TOOL TO ENABLE INTEROPERABILITY IN COASTAL SENSOR NETWORKS". MSSTATE, 2009. http://sun.library.msstate.edu/ETD-db/theses/available/etd-09222009-200303/.
Texto completoHashmi, Muhammad Ali S. M. Massachusetts Institute of Technology. "Said-Huntington Discourse Analyzer : a machine-learning tool for classifying and analyzing discourse". Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/98543.
Texto completoThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 71-74).
Critical discourse analysis (CDA) aims to understand the link "between language and the social" (Mautner and Baker, 2009), and attempts to demystify social construction and power relations (Gramsci, 1999). On the other hand, corpus linguistics deals with principles and practice of understanding the language produced within large amounts of textual data (Oostdijk, 1991). In my thesis, I have aimed to combine, using machine learning, the CDA approach with corpus linguistics with the intention of deconstructing dominant discourses that create, maintain and deepen fault lines between social groups and classes. As an instance of this technological framework, I have developed a tool for understanding and defining the discourse on Islam in the global mainstream media sources. My hypothesis is that the media coverage in several mainstream news sources tends to contextualize Muslims largely as a group embroiled in conflict at a disproportionately large level. My hypothesis is based on the assumption that discourse on Islam in mainstream global media tends to lean toward the dangerous "clash of civilizations" frame. To test this hypothesis, I have developed a prototype tool "Said-Huntington Discourse Analyzer" that machine classifies news articles on a normative scale -- a scale that measures "clash of civilization" polarization in an article on the basis of conflict. The tool also extracts semantically meaningful conversations for a media source using Latent Dirichlet Allocation (LDA) topic modeling, allowing the users to discover frames of conversations on the basis of Said-Huntington index classification. I evaluated the classifier on human-classified articles and found that the accuracy of the classifier was very high (99.03%). Generally, text analysis tools uncover patterns and trends in the data without delineating the 'ideology' that permeates the text. The machine learning tool presented here classifies media discourse on Islam in terms of conflict and non-conflict, and attempts to put light on the 'ideology' that permeates the text. In addition, the tool provides textual analysis of news articles based on the CDA methodologies.
by Muhammad Ali Hashmi.
S.M.
McCoy, Mason Eugene. "A Twitter-Based Prediction Tool for Digital Currency". OpenSIUC, 2018. https://opensiuc.lib.siu.edu/theses/2302.
Texto completoLutero, Gianluca. "A Tool For Data Analysis Using Autoencoders". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20510/.
Texto completoLibros sobre el tema "MACHINE LEARNING TOOL"
Houser, David Allan. Machine learning as a quality improvement tool. Ottawa: National Library of Canada, 1996.
Buscar texto completoBuilding intelligent agents: An apprenticeship multistrategy learning theory, methodology, tool and case studies. San Diego: Academic Press, 1998.
Buscar texto completoKhosrowpour, Mehdi y Information Resources Management Association. Machine learning: Concepts, methodologies, tools and applications. Hershey, PA: Information Science Reference, 2012.
Buscar texto completoEibe, Frank y Hall Mark A, eds. Data mining: Practical machine learning tools and techniques. 3a ed. Burlington, MA: Morgan Kaufmann, 2011.
Buscar texto completoMachine learning: A probabilistic perspective. Cambridge, MA: MIT Press, 2012.
Buscar texto completoCastiello, Maria Elena. Computational and Machine Learning Tools for Archaeological Site Modeling. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-88567-0.
Texto completoPardalos, Panos M., Stamatina Th Rassia y Arsenios Tsokas, eds. Artificial Intelligence, Machine Learning, and Optimization Tools for Smart Cities. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-84459-2.
Texto completoWitten, I. H. Data mining: Practical machine learning tools and techniques with Java implementations. San Francisco, Calif: Morgan Kaufmann, 2000.
Buscar texto completoCapítulos de libros sobre el tema "MACHINE LEARNING TOOL"
Olson, Randal S. y Jason H. Moore. "TPOT: A Tree-Based Pipeline Optimization Tool for Automating Machine Learning". En Automated Machine Learning, 151–60. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05318-5_8.
Texto completoPatil, Nilesh M., Tanmay P. Rane y Anmol A. Panjwani. "ML Suite: An Auto Machine Learning Tool". En Machine Learning for Predictive Analysis, 483–89. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7106-0_48.
Texto completoNguifo, Engelbert Mephu y Patrick Njiwoua. "Using lattice-based framework as a tool for feature extraction". En Machine Learning: ECML-98, 304–9. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0026700.
Texto completoMorin, Emmanuel y Emmanuelle Martienne. "Using a Symbolic Machine Learning Tool to Refine Lexico-syntactic Patterns". En Machine Learning: ECML 2000, 292–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45164-1_31.
Texto completoAnvari, Hamidreza y Paul Lu. "iPerfOPS: A Tool for Machine Learning-Based Optimization Through Protocol Selection". En Machine Learning for Networking, 36–55. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-36183-8_4.
Texto completoHerrmann, Jürgen, Reiner Ackermann, Jörg Peters y Detlef Reipa. "A multistrategy learning system and its integration into an interactive floorplanning tool". En Machine Learning: ECML-94, 138–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/3-540-57868-4_55.
Texto completoInza, Iñaki, Borja Calvo, Rubén Armañanzas, Endika Bengoetxea, Pedro Larrañaga y José A. Lozano. "Machine Learning: An Indispensable Tool in Bioinformatics". En Methods in Molecular Biology, 25–48. Totowa, NJ: Humana Press, 2009. http://dx.doi.org/10.1007/978-1-60327-194-3_2.
Texto completoKokate, Meera B., Bhushan T. Patil y Geetha Subramanian. "Machine Learning as a Smart Manufacturing Tool". En Proceedings of International Conference on Intelligent Manufacturing and Automation, 359–66. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4485-9_37.
Texto completoEl Amine Lazouni, Mohammed, Mostafa El Habib Daho, Nesma Settouti, Mohammed Amine Chikh y Saïd Mahmoudi. "Machine Learning Tool for Automatic ASA Detection". En Modeling Approaches and Algorithms for Advanced Computer Applications, 9–16. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00560-7_5.
Texto completoCacciari, I. y G. F. Pocobelli. "Machine Learning: A Novel Tool for Archaeology". En Handbook of Cultural Heritage Analysis, 961–1002. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-60016-7_33.
Texto completoActas de conferencias sobre el tema "MACHINE LEARNING TOOL"
Chong, Ang Boon, Ch'ng Pei Chun, Cheah Wye Luon, Ngo Seow Yin, Lee Ching Yee, Chin Kevin Che Chau, Ng Kok Aur y Nor Affizal Amirah. "Implementation Tool Machine Learning Features Evaluation". En 2023 IEEE Symposium on Industrial Electronics & Applications (ISIEA). IEEE, 2023. http://dx.doi.org/10.1109/isiea58478.2023.10212351.
Texto completoLi, Ming y Mihai Burzo. "Tool Wear Monitoring Using Machine Learning". En 2021 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE). IEEE, 2021. http://dx.doi.org/10.1109/ccece53047.2021.9569060.
Texto completoHernandez, Alejandra, Clara Gomez, Marina Galli, Jonathan Crespo y Ramon Barber. "PLAYING AND LEARNING TOOL BASED ON MACHINE LEARNING". En 10th annual International Conference of Education, Research and Innovation. IATED, 2017. http://dx.doi.org/10.21125/iceri.2017.0528.
Texto completoLetford, Flynn, Max Rogers, Xun Xu y Yuqian Lu. "Machine Learning to Empower a Cyber-Physical Machine Tool". En 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE). IEEE, 2020. http://dx.doi.org/10.1109/case48305.2020.9216842.
Texto completoSong, Wontaek, In-Wook Oh, Joon-Soo Lee, Chaeeun Kim, Eunseok Nam y Byung-Kwon Min. "Energy Consumption Estimation of Machine Tool Using Machine Learning". En International Conference of Asian Society for Precision Engineering and Nanotechnology. Singapore: Research Publishing Services, 2022. http://dx.doi.org/10.3850/978-981-18-6021-8_or-06-0260.html.
Texto completoLiu, Chia-Ruei, Li-Hua Duan, Po-Wei Chen y Chao-Chun Yang. "Monitoring Machine Tool Based on External Physical Characteristics of the Machine Tool Using Machine Learning Algorithm". En 2018 First International Conference on Artificial Intelligence for Industries (AI4I). IEEE, 2018. http://dx.doi.org/10.1109/ai4i.2018.8665696.
Texto completoMoysen, Jessica, Lorenza Giupponi y Josep Mangues-Bafalluy. "A machine learning enabled network planning tool". En 2016 IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC). IEEE, 2016. http://dx.doi.org/10.1109/pimrc.2016.7794909.
Texto completoLiu, Yelin, Yang Liu, Tsong Yueh Chen y Zhi Quan Zhou. "A Testing Tool for Machine Learning Applications". En ICSE '20: 42nd International Conference on Software Engineering. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3387940.3392694.
Texto completoLi, Wei, Ming Yang y Zi-cai Wang. "Flexible Simulation Data Collection and Replay Tool". En 2006 International Conference on Machine Learning and Cybernetics. IEEE, 2006. http://dx.doi.org/10.1109/icmlc.2006.258607.
Texto completoWu, Yu-Chuan y Zhi-Yong Zuo. "Temperature Intelligent Control System Research for Quencher Machine Tool". En 2007 International Conference on Machine Learning and Cybernetics. IEEE, 2007. http://dx.doi.org/10.1109/icmlc.2007.4370236.
Texto completoInformes sobre el tema "MACHINE LEARNING TOOL"
Ludwig, Jens y Sendhil Mullainathan. Machine Learning as a Tool for Hypothesis Generation. Cambridge, MA: National Bureau of Economic Research, marzo de 2023. http://dx.doi.org/10.3386/w31017.
Texto completoXie, Bin. DiagSoftfailure: Automated Soft-Failure Diagnostic Tool Using Machine Learning for Network Users. Office of Scientific and Technical Information (OSTI), noviembre de 2019. http://dx.doi.org/10.2172/1575995.
Texto completoYoo, Shinjae, Yonggang Cui, Ji Hwan Park, Yuewei Lin y Yihui Ren. Development of a software tool for IAEA use of the YOLOv3 machine learning algorithm. Office of Scientific and Technical Information (OSTI), febrero de 2019. http://dx.doi.org/10.2172/1494041.
Texto completoChou, Roger, Tracy Dana y Kanaka D. Shetty. Testing a Machine Learning Tool for Facilitating Living Systematic Reviews of Chronic Pain Treatments. Agency for Healthcare Research and Quality (AHRQ), noviembre de 2020. http://dx.doi.org/10.23970/ahrqepcmethtestingmachinelearning.
Texto completoFrash, Luke y Bulbul Ahmmed. GeoThermalCloud: A Machine Learning Tool for Discovery, Exploration, and Development of Hidden Geothermal Resources. Office of Scientific and Technical Information (OSTI), septiembre de 2023. http://dx.doi.org/10.2172/2007326.
Texto completoAlonso-Robisco, Andrés, José Manuel Carbó y José Manuel Carbó. Machine Learning methods in climate finance: a systematic review. Madrid: Banco de España, febrero de 2023. http://dx.doi.org/10.53479/29594.
Texto completoGates, Allison, Michelle Gates, Shannon Sim, Sarah A. Elliott, Jennifer Pillay y Lisa Hartling. Creating Efficiencies in the Extraction of Data From Randomized Trials: A Prospective Evaluation of a Machine Learning and Text Mining Tool. Agency for Healthcare Research and Quality (AHRQ), agosto de 2021. http://dx.doi.org/10.23970/ahrqepcmethodscreatingefficiencies.
Texto completoLasko, Kristofer y Elena Sava. Semi-automated land cover mapping using an ensemble of support vector machines with moderate resolution imagery integrated into a custom decision support tool. Engineer Research and Development Center (U.S.), noviembre de 2021. http://dx.doi.org/10.21079/11681/42402.
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 completoHarris, Philip. Physics Community Needs, Tools, and Resources for Machine Learning. Office of Scientific and Technical Information (OSTI), marzo de 2022. http://dx.doi.org/10.2172/1873720.
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