Tesis sobre el tema "MACHINE ALGORITHMS"
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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.
Johansson, Samuel y Karol Wojtulewicz. "Machine learning algorithms in a distributed context". Thesis, Linköpings universitet, Institutionen för datavetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-148920.
Texto completoShen, Chenyang. "Regularized models and algorithms for machine learning". HKBU Institutional Repository, 2015. https://repository.hkbu.edu.hk/etd_oa/195.
Texto completoChoudhury, A. "Fast machine learning algorithms for large data". Thesis, University of Southampton, 2002. https://eprints.soton.ac.uk/45907/.
Texto completoWesterlund, Fredrik. "CREDIT CARD FRAUD DETECTION (Machine learning algorithms)". Thesis, Umeå universitet, Statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-136031.
Texto completoLi, Yunming. "Machine vision algorithms for mining equipment automation". Thesis, Queensland University of Technology, 2000.
Buscar texto completoLiu, Ming. "Design and Evaluation of Algorithms for Online Machine Scheduling Problems". Phd thesis, Ecole Centrale Paris, 2009. http://tel.archives-ouvertes.fr/tel-00453316.
Texto completoThompson, Simon Giles. "Distributed boosting algorithms". Thesis, University of Portsmouth, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.285529.
Texto completoWang, Gang. "Solution path algorithms : an efficient model selection approach /". View abstract or full-text, 2007. http://library.ust.hk/cgi/db/thesis.pl?CSED%202007%20WANGG.
Texto completoLi, Xiao. "Regularized adaptation : theory, algorithms, and applications /". Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/5928.
Texto completoKalyanasundaram, Subrahmanyam. "Turing machine algorithms and studies in quasi-randomness". Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/42808.
Texto completoJanagam, Anirudh y Saddam Hossen. "Analysis of Network Intrusion Detection System with Machine Learning Algorithms (Deep Reinforcement Learning Algorithm)". Thesis, Blekinge Tekniska Högskola, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-17126.
Texto completoTorcolacci, Veronica. "Implementation of Machine Learning Algorithms on Hardware Accelerators". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.
Buscar texto completoLim, Choon Kee. "Hypercube machine implementation of low-level vision algorithms". Ohio : Ohio University, 1989. http://www.ohiolink.edu/etd/view.cgi?ohiou1182864143.
Texto completoOuyang, Hua. "Optimal stochastic and distributed algorithms for machine learning". Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/49091.
Texto completoOdetayo, Michael Omoniyi. "On genetic algorithms in machine learning and optimisation". Thesis, University of Strathclyde, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.239866.
Texto completoSengupta, Sudipta 1974. "Algorithms and approximation schemes for machine scheduling problems". Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/80240.
Texto completoBrunning, James Jonathan Jesse. "Alignment models and algorithms for statistical machine translation". Thesis, University of Cambridge, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.608922.
Texto completoAl-Abri, Eman S. "Modelling atmospheric ozone concentration using machine learning algorithms". Thesis, Loughborough University, 2016. https://dspace.lboro.ac.uk/2134/25091.
Texto completoLim, Choon Kee. "Hypercube machine implementation of low-level vision algorithms". Ohio University / OhioLINK, 1988. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1182864143.
Texto completoDabert, Geoffrey. "Application of Machine Learning techniques to Optimization algorithms". Thesis, KTH, Optimeringslära och systemteori, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-207471.
Texto completoAwe, Olusegun P. "Machine learning algorithms for cognitive radio wireless networks". Thesis, Loughborough University, 2015. https://dspace.lboro.ac.uk/2134/19609.
Texto completoGranström, Daria y Johan Abrahamsson. "Loan Default Prediction using Supervised Machine Learning Algorithms". Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252312.
Texto completoDet är nödvändigt för en bank att ha en bra uppskattning på hur stor risk den bär med avseende på kunders fallissemang. Olika statistiska metoder har använts för att estimera denna risk, men med den nuvarande utvecklingen inom maskininlärningsområdet har det väckt ett intesse att utforska om maskininlärningsmetoder kan förbättra kvaliteten på riskuppskattningen. Syftet med denna avhandling är att undersöka vilken metod av de implementerade maskininlärningsmetoderna presterar bäst för modellering av fallissemangprediktion med avseende på valda modelvaldieringsparametrar. De implementerade metoderna var Logistisk Regression, Random Forest, Decision Tree, AdaBoost, XGBoost, Artificiella neurala nätverk och Stödvektormaskin. En översamplingsteknik, SMOTE, användes för att behandla obalansen i klassfördelningen för svarsvariabeln. Resultatet blev följande: XGBoost utan implementering av SMOTE visade bäst resultat med avseende på den valda metriken.
Lubbe, H. G. y B. J. Kotze. "Machine learning through self generating programs". Interim : Interdisciplinary Journal, Vol 6, Issue 2: Central University of Technology Free State Bloemfontein, 2007. http://hdl.handle.net/11462/407.
Texto completoPeople have tried different ways to make machines intelligent. One option is to use a simulated neural net as a platform for Genetic Algorithms. Neural nets are a combination of neurons in a certain pattern. Neurons in a neural net system are a simulation of neurons in an organism's brain. Genetic Algorithms represent an emulation of evolution in nature. The question arose as to why write a program to simulate neurons if a program can execute the functions a combination of neurons would generate. For this reason a virtual robot indicated in Figure 1 was made "intelligent" by developing a process where the robot creates a program for itself. Although Genetic Algorithms might have been used in the past to generate a program, a new method called Single-Chromosome-Evolution-Algorithms (SCEA) was introduced and compared to Genetic Algorithms operation. Instructions in the program were changed by using either Genetic Algorithms or alternatively with SCEA where only one simulation was needed per generation to be tested by the fitness of the system.
Tu, Zhuozhuo. "Towards Robust and Reliable Machine Learning: Theory and Algorithms". Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/28832.
Texto completoGranek, Justin. "Application of machine learning algorithms to mineral prospectivity mapping". Thesis, University of British Columbia, 2016. http://hdl.handle.net/2429/59988.
Texto completoScience, Faculty of
Earth, Ocean and Atmospheric Sciences, Department of
Graduate
Chen, Tracy Lin. "Performance of ordinal algorithms for parallel machine scheduling problems". Thesis, University of Ottawa (Canada), 1994. http://hdl.handle.net/10393/6458.
Texto completoArtchounin, Daniel. "Tuning of machine learning algorithms for automatic bug assignment". Thesis, Linköpings universitet, Programvara och system, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-139230.
Texto completoDarnald, Johan. "Predicting Attrition in Financial Data with Machine Learning Algorithms". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-225852.
Texto completoFör de flesta företag finns det en kostnad involverad i att skaffa nya kunder. Längre relationer med kunder är därför ofta mer lönsamma. Att kunna förutsäga om en kund är nära att lämna företaget är därför ett användbart verktyg för att kunna utföra åtgärder för att minska denna kostnad. Händelsen när en kund avslutar sin relation med ett företag kallas här efter kundförlust. Att förutsäga människors handlingar är däremot svårt och många olika faktorer kan påverka deras val. Denna avhandling undersöker olika maskininlärningsmetoder för att förutsäga kundförluster hos en bank. Fyra metoder väljs baserat på tidigare forskning och dessa testas och jämförs sedan för att hitta vilken som fungerar bäst för att förutsäga dessa händelser. Fyra dataset från två olika produkter och med två olika användningsområden skapas från verklig data ifrån en Europeisk bank. Alla metoder tränas och testas på varje dataset. Resultaten från dessa test utvärderas och jämförs sedan för att få reda på vilken metod som fungerar bäst. Metoderna som enligt tidigare forskning ger de mest pålitliga och bästa resultaten för att förutsäga kundförluster hos banker är stödvektormaskin, neurala nätverk, balanserad slumpmässig skog och vägd slumpmässig skog. Resultatet av testerna visar att en balanserad slumpmässig skog får bäst resultat med en genomsnittlig AUC på 0.698 och ett F-värde på 0.376. Träffsäkerheten och det positiva prediktiva värdet på metoden är inte tillräckligt för att ta definitiva handlingar med men kan användas med andra faktorer så som lönsamhetsuträkningar för att förbättra effektiviteten av handlingar som tas för att minska de negativa effekterna av kundförluster.
Raykar, Vikas Chandrakant. "Scalable machine learning for massive datasets fast summation algorithms /". College Park, Md. : University of Maryland, 2007. http://hdl.handle.net/1903/6797.
Texto completoThesis research directed by: Computer Science. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
Xu, Yi-Chang. "Parallel thinning algorithms and their implementation on hypercube machine". Ohio : Ohio University, 1991. http://www.ohiolink.edu/etd/view.cgi?ohiou1183989550.
Texto completoPoke, Marius [Verfasser]. "Algorithms for High-Performance State-Machine Replication / Marius Poke". Hamburg : Helmut-Schmidt-Universität, Bibliothek, 2019. http://d-nb.info/1192766512/34.
Texto completoIbrahim, Osman Ali Sadek. "Evolutionary algorithms and machine learning techniques for information retrieval". Thesis, University of Nottingham, 2017. http://eprints.nottingham.ac.uk/47696/.
Texto completoShah, Niyati S. "Implementing Machine Learning Algorithms for Identifying Microstructure of Materials". Thesis, California State University, Long Beach, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10837912.
Texto completoAlloys of different materials are extensively used in many fields of our day-to-day life. Several studies are performed at a microscopic level to analyze the properties of such alloys. Manually evaluating these microscopic structures (microstructures) can be time-consuming. This thesis attempts to build different models that can automate the identification of an alloy from its microstructure. All the models were developed, with various supervised and unsupervised machine learning algorithms, and results of all the models were compared. The best accuracy of 92.01 ? 0.54% and 94.31 ? 0.59% was achieved, for identifying the type of an alloy from its microstructure (Task 1) and classifying the microstructure as belonging to either Ferrous, Non-Ferrous or Others class (Task 2), respectively. The model, which gave the best accuracy, was then used to build an Image Search Engine (ISE) that can predict the type of an alloy from its microstructure, search the microstructures by different keywords and search for visually similar microstructures.
Johansson, David. "Price Prediction of Vinyl Records Using Machine Learning Algorithms". Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-96464.
Texto completoVandehzad, Mashhood. "Efficient flight schedules with utilizing Machine Learning prediction algorithms". Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20663.
Texto completoRoychowdhury, Anirban. "Robust and Scalable Algorithms for Bayesian Nonparametric Machine Learning". The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1511901271093727.
Texto completoLiang, Jiongqian. "Human-in-the-loop Machine Learning: Algorithms and Applications". The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1523988406039076.
Texto completoLanctot, J. Kevin (Joseph Kevin) Carleton University Dissertation Mathematics. "Discrete estimator algorithms: a mathematical model of machine learning". Ottawa, 1989.
Buscar texto completoLi, Ling Abu-Mostafa Yaser S. "Data complexity in machine learning and novel classification algorithms /". Diss., Pasadena, Calif. : Caltech, 2006. http://resolver.caltech.edu/CaltechETD:etd-04122006-114210.
Texto completoBäckman, David. "EVALUATION OF MACHINE LEARNING ALGORITHMS FOR SMS SPAM FILTERING". Thesis, Umeå universitet, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-163188.
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