Literatura académica sobre el tema "MACHINE ALGORITHMS"
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Artículos de revistas sobre el tema "MACHINE ALGORITHMS"
Mishra, Akshansh y Apoorv Vats. "Supervised Machine Learning Classification Algorithms for Detection of Fracture Location in Dissimilar Friction Stir Welded Joints". Frattura ed Integrità Strutturale 15, n.º 58 (25 de septiembre de 2021): 242–53. http://dx.doi.org/10.3221/igf-esis.58.18.
Texto completoBenbouzid, Bilel. "Unfolding Algorithms". Science & Technology Studies 32, n.º 4 (13 de diciembre de 2019): 119–36. http://dx.doi.org/10.23987/sts.66156.
Texto completoHE, YONG, SHUGUANG HAN y YIWEI JIANG. "ONLINE ALGORITHMS FOR SCHEDULING WITH MACHINE ACTIVATION COST". Asia-Pacific Journal of Operational Research 24, n.º 02 (abril de 2007): 263–77. http://dx.doi.org/10.1142/s0217595907001231.
Texto completoTURAN, SELIN CEREN y MEHMET ALI CENGIZ. "ENSEMBLE LEARNING ALGORITHMS". Journal of Science and Arts 22, n.º 2 (30 de junio de 2022): 459–70. http://dx.doi.org/10.46939/j.sci.arts-22.2-a18.
Texto completoLing, Qingyang. "Machine learning algorithms review". Applied and Computational Engineering 4, n.º 1 (14 de junio de 2023): 91–98. http://dx.doi.org/10.54254/2755-2721/4/20230355.
Texto completoSameer, S. K. L. y P. Sriramya. "Improving the Efficiency by Novel Feature Extraction Technique Using Decision Tree Algorithm Comparing with SVM Classifier Algorithm for Predicting Heart Disease". Alinteri Journal of Agriculture Sciences 36, n.º 1 (29 de junio de 2021): 713–20. http://dx.doi.org/10.47059/alinteri/v36i1/ajas21100.
Texto completoMeena, Munesh y Ruchi Sehrawat. "Breakdown of Machine Learning Algorithms". Recent Trends in Artificial Intelligence & it's Applications 1, n.º 3 (16 de octubre de 2022): 25–29. http://dx.doi.org/10.46610/rtaia.2022.v01i03.005.
Texto completoMaitre, Julien, Sébastien Gaboury, Bruno Bouchard y Abdenour Bouzouane. "A Black-Box Model for Estimation of the Induction Machine Parameters Based on Stochastic Algorithms". International Journal of Monitoring and Surveillance Technologies Research 3, n.º 3 (julio de 2015): 44–67. http://dx.doi.org/10.4018/ijmstr.2015070103.
Texto completoCastelo, Noah, Maarten W. Bos y Donald Lehmann. "Let the Machine Decide: When Consumers Trust or Distrust Algorithms". NIM Marketing Intelligence Review 11, n.º 2 (1 de noviembre de 2019): 24–29. http://dx.doi.org/10.2478/nimmir-2019-0012.
Texto completoK.M., Umamaheswari. "Road Accident Perusal Using Machine Learning Algorithms". International Journal of Psychosocial Rehabilitation 24, n.º 5 (31 de marzo de 2020): 1676–82. http://dx.doi.org/10.37200/ijpr/v24i5/pr201839.
Texto completoTesis sobre el tema "MACHINE ALGORITHMS"
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.
Romano, 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 completoMoon, Gordon Euhyun. "Parallel Algorithms for Machine Learning". The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1561980674706558.
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 completoSahoo, Shibashankar. "Soft machine : A pattern language for interacting with machine learning algorithms". Thesis, Umeå universitet, Designhögskolan vid Umeå universitet, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-182467.
Texto completoDunkelberg, Jr John S. "FEM Mesh Mapping to a SIMD Machine Using Genetic Algorithms". Digital WPI, 2001. https://digitalcommons.wpi.edu/etd-theses/1154.
Texto completoWilliams, Cristyn Barry. "Colour constancy : human mechanisms and machine algorithms". Thesis, City University London, 1995. http://openaccess.city.ac.uk/7731/.
Texto completoMitchell, Brian. "Prepositional phrase attachment using machine learning algorithms". Thesis, University of Sheffield, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.412729.
Texto completoPASSOS, BRUNO LEONARDO KMITA DE OLIVEIRA. "SCHEDULING ALGORITHMS APPLICATION FOR MACHINE AVAILABILITY CONSTRAINT". PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2014. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=24311@1.
Texto completoCOORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE EXCELENCIA ACADEMICA
Grande parte da literatura de problemas de escalonamento assume que todas as máquinas estão disponíveis durante todo o período de análise o que, na prática, não é verdade, pois algumas das máquinas podem estar indisponíveis para processamento sem aviso prévio devido a problemas ou a políticas de utilização de seus recursos. Nesta tese, exploramos algumas das poucas heurísticas disponíveis na literatura para a minimização do makespan para este tipo de problema NP-difícil e apresentamos uma nova heurística que utiliza estatísticas de disponibilidade das máquinas para gerar um escalonamento. O estudo experimental com dados reais mostrou que a nova heurística apresenta ganhos de makespan em relação aos demais algoritmos clássicos que não utilizam informações de disponibilidade no processo de decisão. A aplicação prática deste problema está relacionada a precificação de ativos de uma carteira teórica de forma a estabelecer o risco de mercado da forma mais rápida possível através da utilização de recursos tecnológicos ociosos.
Most literature in scheduling theory assumes that machines are always available during the scheduling time interval, which in practice is not true due to machine breakdowns or resource usage policies. We study a few available heuristics for the NP-hard problem of minimizing the makespan when breakdowns may happen. We also develop a new scheduling heuristic based on historical machine availability information. Our experimental study, with real data, suggests that this new heuristic is better in terms of makespan than other algorithms that do not take this information into account. We apply the results of our investigation for the asset-pricing problem of a fund portfolio in order to determine a full valuation market risk using idle technological resources of a company.
Wen, Tong 1970. "Support Vector Machine algorithms : analysis and applications". Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/8404.
Texto completoIncludes bibliographical references (p. 89-97).
Support Vector Machines (SVMs) have attracted recent attention as a learning technique to attack classification problems. The goal of my thesis work is to improve computational algorithms as well as the mathematical understanding of SVMs, so that they can be easily applied to real problems. SVMs solve classification problems by learning from training examples. From the geometry, it is easy to formulate the finding of SVM classifiers as a linearly constrained Quadratic Programming (QP) problem. However, in practice its dual problem is actually computed. An important property of the dual QP problem is that its solution is sparse. The training examples that determine the SVM classifier are known as support vectors (SVs). Motivated by the geometric derivation of the primal QP problem, we investigate how the dual problem is related to the geometry of SVs. This investigation leads to a geometric interpretation of the scaling property of SVMs and an algorithm to further compress the SVs. A random model for the training examples connects the Hessian matrix of the dual QP problem to Wishart matrices. After deriving the distributions of the elements of the inverse Wishart matrix Wn-1(n, nI), we give a conjecture about the summation of the elements of Wn-1(n, nI). It becomes challenging to solve the dual QP problem when the training set is large. We develop a fast algorithm for solving this problem. Numerical experiments show that the MATLAB implementation of this projected Conjugate Gradient algorithm is competitive with benchmark C/C++ codes such as SVMlight and SvmFu. Furthermore, we apply SVMs to time series data.
(cont.) In this application, SVMs are used to predict the movement of the stock market. Our results show that using SVMs has the potential to outperform the solution based on the most widely used geometric Brownian motion model of stock prices.
by Tong Wen.
Ph.D.
Libros sobre el tema "MACHINE ALGORITHMS"
Li, Fuwei, Lifeng Lai y Shuguang Cui. Machine Learning Algorithms. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16375-3.
Texto completoAyyadevara, V. Kishore. Pro Machine Learning Algorithms. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3564-5.
Texto completoArnold, Schönhage. Fast algorithms: A multitape Turing machine implementation. Mannheim: B.I. Wissenschaftsverlag, 1994.
Buscar texto completoWhelan, Paul F. y Derek Molloy. Machine Vision Algorithms in Java. London: Springer London, 2001. http://dx.doi.org/10.1007/978-1-4471-0251-9.
Texto completoGrefenstette, John J., ed. Genetic Algorithms for Machine Learning. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4615-2740-4.
Texto completoMandal, Jyotsna Kumar, Somnath Mukhopadhyay, Paramartha Dutta y Kousik Dasgupta, eds. Algorithms in Machine Learning Paradigms. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1041-0.
Texto completoMachine vision: theory, algorithms, practicalities. London: Academic, 1990.
Buscar texto completoDavies, E. R. Machine vision: Theory, algorithms, practicalities. 3a ed. Amsterdam: Elsevier, 2005.
Buscar texto completoJ, Grefenstette John, ed. Genetic algorithms for machine learning. Boston: Kluwer Academic Publishers, 1994.
Buscar texto completoPaliouras, Georgios. Scalability of machine learning algorithms. Manchester: University of Manchester, 1993.
Buscar texto completoCapítulos de libros sobre el tema "MACHINE ALGORITHMS"
Geetha, T. V. y S. Sendhilkumar. "Classification Algorithms". En Machine Learning, 127–51. Boca Raton: Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003290100-6.
Texto completoBrucker, Peter. "Single Machine Scheduling Problems". En Scheduling Algorithms, 61–106. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24804-0_4.
Texto completoBrucker, Peter. "Single Machine Scheduling Problems". En Scheduling Algorithms, 61–106. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04550-3_4.
Texto completoBrucker, Peter. "Single Machine Scheduling Problems". En Scheduling Algorithms, 61–100. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-662-03612-9_4.
Texto completoBrucker, Peter. "Single Machine Scheduling Problems". En Scheduling Algorithms, 60–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-662-03088-2_4.
Texto completoPendyala, Vishnu. "Machine Learning Algorithms". En Veracity of Big Data, 87–118. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3633-8_5.
Texto completoPanesar, Arjun. "Machine Learning Algorithms". En Machine Learning and AI for Healthcare, 119–88. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-3799-1_4.
Texto completoSteger, Carsten. "Machine Vision Algorithms". En Handbook of Machine and Computer Vision, 505–698. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2017. http://dx.doi.org/10.1002/9783527413409.ch9.
Texto completoPanesar, Arjun. "Machine Learning Algorithms". En Machine Learning and AI for Healthcare, 85–144. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6537-6_4.
Texto completoZhou, Ding-Xuan. "Machine Learning Algorithms". En Encyclopedia of Applied and Computational Mathematics, 839–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-540-70529-1_301.
Texto completoActas de conferencias sobre el tema "MACHINE ALGORITHMS"
Wang, Yingfeng, Zhijing Liu y Wei Yan. "Algorithms for Random Adjacency Matrixes Generation Used for Scheduling Algorithms Test". En 2010 International Conference on Machine Vision and Human-machine Interface. IEEE, 2010. http://dx.doi.org/10.1109/mvhi.2010.190.
Texto completoArden, Farel y Cutifa Safitri. "Hyperparameter Tuning Algorithm Comparison with Machine Learning Algorithms". En 2022 6th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE). IEEE, 2022. http://dx.doi.org/10.1109/icitisee57756.2022.10057630.
Texto completoTeixeira, L. P., W. Celes y M. Gattass. "Accelerated Corner-Detector Algorithms". En British Machine Vision Conference 2008. British Machine Vision Association, 2008. http://dx.doi.org/10.5244/c.22.62.
Texto completoNarendra, Pat. "VLSI Architectures for Real-Time Image Processing". En Machine Vision. Washington, D.C.: Optica Publishing Group, 1985. http://dx.doi.org/10.1364/mv.1985.fd4.
Texto completoShabdirova, Ainash, Ashirgul Kozhagulova, Minh Nguyen y Yong Zhao. "A Novel Approach to Sand Volume Prediction Using Machine Learning Algorithms". En International Petroleum Technology Conference. IPTC, 2023. http://dx.doi.org/10.2523/iptc-22770-ea.
Texto completoHalyo, Nesim y Richard W. Samms. "Combined Optimization of Image Gathering Optics and Image Processing Algorithm for Edge Detection". En Machine Vision. Washington, D.C.: Optica Publishing Group, 1985. http://dx.doi.org/10.1364/mv.1985.thd1.
Texto completoAbdullahi, M. I., G. I. O. Aimufua y U. A. Muhammad. "Application of Sales Forecasting Model Based on Machine Learning Algorithms." En 28th iSTEAMS Multidisciplinary Research Conference AIUWA The Gambia. Society for Multidisciplinary and Advanced Research Techniques - Creative Research Publishers, 2021. http://dx.doi.org/10.22624/aims/isteams-2021/v28p17.
Texto completoCourtney, P., R. B. Yates y P. A. Ivey. "Mapping Algorithms on to Platforms: An Approach to Algorithm and Hardware Co-Design". En British Machine Vision Conference 1994. British Machine Vision Association, 1994. http://dx.doi.org/10.5244/c.8.79.
Texto completoGarnica, O. "Finite state machine optimization using genetic algorithms". En Second International Conference on Genetic Algorithms in Engineering Systems. IEE, 1997. http://dx.doi.org/10.1049/cp:19971194.
Texto completoKhan, Rehan Ullah y Saleh Albahli. "Machine Learning Augmentation". En ACAI 2019: 2019 2nd International Conference on Algorithms, Computing and Artificial Intelligence. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3377713.3377726.
Texto completoInformes sobre el tema "MACHINE ALGORITHMS"
Stepp, Robert E., Bradley L. Whitehall y Lawrence B. Holder. Toward Intelligent Machine Learning Algorithms. Fort Belvoir, VA: Defense Technical Information Center, mayo de 1988. http://dx.doi.org/10.21236/ada197049.
Texto completoCaravelli, Francesco. Towards memristor supremacy with novel machine learning algorithms. Office of Scientific and Technical Information (OSTI), septiembre de 2021. http://dx.doi.org/10.2172/1822713.
Texto completoDim, Odera, Carlos Soto, Yonggang Cui, Lap-Yan Cheng, Maia Gemmill, Thomas Grice, Joseph Rivers, Warren Stern y Michael Todosow. VERIFICATION OF TRISO FUEL BURNUP USING MACHINE LEARNING ALGORITHMS. Office of Scientific and Technical Information (OSTI), agosto de 2021. http://dx.doi.org/10.2172/1813329.
Texto completoWaldrop, Lauren, Carl Hart, Nancy Parker, Chris Pettit y Scotland McIntosh. Utility of machine learning algorithms for natural background photo classification. Cold Regions Research and Engineering Laboratory (U.S.), junio de 2018. http://dx.doi.org/10.21079/11681/27344.
Texto completoGrechanuk, Pavel, Michael Rising y Todd Palmer. Application of Machine Learning Algorithms to Identify Problematic Nuclear Data. Office of Scientific and Technical Information (OSTI), enero de 2021. http://dx.doi.org/10.2172/1906466.
Texto completoBissett, W. P. Optimizing Machine Learning Algorithms For Hyperspectral Very Shallow Water (VSW) Products. Fort Belvoir, VA: Defense Technical Information Center, enero de 2009. http://dx.doi.org/10.21236/ada531071.
Texto completoBissett, W. P. Optimizing Machine Learning Algorithms for Hyperspectral Very Shallow Water (VSW) Products. Fort Belvoir, VA: Defense Technical Information Center, junio de 2009. http://dx.doi.org/10.21236/ada504929.
Texto completoBissett, W. P. Optimizing Machine Learning Algorithms for Hyperspectral Very Shallow Water (VSW) Products. Fort Belvoir, VA: Defense Technical Information Center, enero de 2008. http://dx.doi.org/10.21236/ada516714.
Texto completoPoczos, Barnabas. Machine Learning Algorithms for Matching Theories, Simulations, and Observations in Cosmology. Office of Scientific and Technical Information (OSTI), diciembre de 2018. http://dx.doi.org/10.2172/1572709.
Texto completoHerrera, Allen, Eugene Moore y Alexander Heifetz. Development of Gamma Background Radiation Digital Twin with Machine Learning Algorithms. Office of Scientific and Technical Information (OSTI), noviembre de 2020. http://dx.doi.org/10.2172/1735365.
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