Relevant bibliographies by topics / Applied learning / Dissertations / Theses
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Dissertations / Theses on the topic 'Applied learning'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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1

Mauricio, Palacio Sebastián. "Machine-Learning Applied Methods." Doctoral thesis, Universitat de Barcelona, 2020. http://hdl.handle.net/10803/669286.

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The presented discourse followed several topics where every new chapter introduced an economic prediction problem and showed how traditional approaches can be complemented with new techniques like machine learning and deep learning. These powerful tools combined with principles of economic theory is highly increasing the scope for empiricists. Chapter 3 addressed this discussion. By progressively moving from Ordinary Least Squares, Penalized Linear Regressions and Binary Trees to advanced ensemble trees. Results showed that ML algorithms significantly outperform statistical models in terms of
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Galimberti, Jaqueson Kingeski. "Adaptive learning for applied macroeconomics." Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/adaptive-learning-for-applied-macroeconomics(cde517d7-d552-4a53-a442-c584262c3a8f).html.

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The literature on bounded rationality and learning in macroeconomics has often used recursive algorithms to depict the evolution of agents' beliefs over time. In this thesis we assess this practice from an applied perspective, focusing on the use of such algorithms for the computation of forecasts of macroeconomic variables. Our analysis develops around three issues we find to have been previously neglected in the literature: (i) the initialization of the learning algorithms; (ii) the determination and calibration of the learning gains, which are key parameters of the algorithms' specification
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Bargagli, Stoffi Falco Johannes. "Essays on applied machine learning." Thesis, IMT Alti Studi Lucca, 2020. http://e-theses.imtlucca.it/311/1/BargagliStoffi_phdthesis.pdf.

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In this Dissertation, we deal with a series of applications of machine learning in the fields of social and health sciences. We introduce a set of novelties in the traditional usage of machine learning algorithms for predictive and causal inference tasks. In Part 1, we explore the field of machine learning for causal inference and we introduce two innovative techniques that combine state-of-the-art machine learning algorithms with causal inference methodologies. In the first Chapter, we introduce a novel Bayesian tree-based methodology to draw causal inference on heterogeneous effects
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Serafini, Sara. "Machine Learning applied to OCR tasks." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019.

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The content of this thesis describes the work done during a six-month internship at Datalogic, in its research laboratories in Pasadena (CA). The aim of my research was to implement and evaluate a classifier as part of an industrial OCR system for learning purposes and to see how well it could work in comparison to current best Datalogic products, since it might be simpler/faster, it might be a good alternative for implementing on an embedded system (where current Datalogic products may not be able to run fast enough).
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Mohd, Nawi Abdullah. "Applied Drama in English Language Learning." Thesis, University of Canterbury. School of Literacies and Arts in Education, 2014. http://hdl.handle.net/10092/9584.

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This thesis is a reflective exploration of the use and impact of using drama pedagogies in the English as a Second Language (ESL)/ English as a Foreign Language (EFL) classroom. It stems from the problem of secondary school English language learning in Malaysia, where current teaching practices appear to have led to the decline of the standard of English as a second language in school leavers and university graduates (Abdul Rahman, 1997; Carol Ong Teck Lan, Anne Leong Chooi Khaun, & Singh, 2011; Hazita et al., 2010; Nalliah & Thiyagarajah, 1999). This problem resonates with my own experiences
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Drake, Adam C. "Practical Improvements in Applied Spectral Learning." BYU ScholarsArchive, 2010. https://scholarsarchive.byu.edu/etd/2546.

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Spectral learning algorithms, which learn an unknown function by learning a spectral representation of the function, have been widely used in computational learning theory to prove many interesting learnability results. These algorithms have also been successfully used in real-world applications. However, previous work has left open many questions about how to best use these methods in real-world learning scenarios. This dissertation presents several significant advances in real-world spectral learning. It presents new algorithms for finding large spectral coefficients (a key sub-problem in sp
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Domeniconi, Federico. "Deep Learning Techniques applied to Photometric Stereo." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20031/.

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La tesi si focalizza sullo studio dello stato dell’arte della fotometria stereo con deep learning: Self-calibrating Deep Photometric Stereo Networks. Il modello è composto è composto di due reti, la prima predice la direzione e l’intensità delle luci, la seconda predice le normali della superficie. L’obiettivo della tesi è individuare i limiti del modello e capire se possa essere modifcato per avere buone prestazioni anche in scenari reali. Il progetto di tesi è basato su fine-tuning, una tecnica supervisionata di transfer learning. Per questo scopo un nuovo dataset è stato creato acquisendo i
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Wang, Shihai. "Boosting learning applied to facial expression recognition." Thesis, University of Manchester, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.511940.

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Weintraub, Ben Julian. "Learning control applied to a model helicopter." Thesis, Massachusetts Institute of Technology, 1994. http://hdl.handle.net/1721.1/49921.

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Idowu, Samuel O. "Applied Machine Learning in District Heating System." Licentiate thesis, Luleå tekniska universitet, Datavetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-68486.

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In an increasingly applied domain of pervasive computing, sensing devices are being deployed progressively for data acquisition from various systems through the use of technologies such as wireless sensor networks. Data obtained from such systems are used analytically to advance or improve system performance or efficiency. The possibility to acquire an enormous amount of data from any target system has made machine learning a useful approach for several large-scale analytical solutions. Machine learning has proved viable in the area of the energy sector, where the global demand for energy and
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Martin, del Campo Barraza Sergio. "Unsupervised feature learning applied to condition monitoring." Doctoral thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-63113.

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Improving the reliability and efficiency of rotating machinery are central problems in many application domains, such as energy production and transportation. This requires efficient condition monitoring methods, including analytics needed to predict and detect faults and manage the high volume and velocity of data. Rolling element bearings are essential components of rotating machines, which are particularly important to monitor due to the high requirements on the operational conditions. Bearings are also located near the rotating parts of the machines and thereby the signal sources that char
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Poggi, Matteo <1990&gt. "Machine Learning techniques applied to stereo vision." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amsdottorato.unibo.it/8444/1/thesis.pdf.

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Stereo is a popular technique enabling fast and dense depth estimation from two or more images. Its success is mainly due to its easiness of deployment, requiring only a couple or multiple synchronized image sensors, accurately calibrated to solve the matching problem between pixels on one of the images (named reference) and the other (named target). The absence of active technologies (e.g. pattern projection, laser scanners etc..) make this solution deployable on almost every scenario. Despite the wide literature concerning stereo, it still represents an open problem because of very challen
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CAZZATO, STEFANIA. "Machine Learning Techniques Applied to Telecommunication Data." Doctoral thesis, Università degli studi di Genova, 2019. http://hdl.handle.net/11567/942536.

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Finnman, Peter, and Max Winberg. "Deep reinforcement learning compared with Q-table learning applied to backgammon." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186545.

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Reinforcement learning attempts to mimic how humans react to their surrounding environment by giving feedback to software agents based on the actions they take. To test the capabilities of these agents, researches have long regarded board games as a powerful tool. This thesis compares two approaches to reinforcement learning in the board game backgammon, a Q-table and a deep reinforcement network. It was determined which approach surpassed the other in terms of accuracy and convergence rate towards the perceived optimal strategy. The evaluation is performed by training the agents using the sel
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Amar, Gilad. "Deep learning for supernovae detection." Master's thesis, University of Cape Town, 2017. http://hdl.handle.net/11427/27090.

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In future astronomical sky surveys it will be humanly impossible to classify the tens of thousands of candidate transients detected per night. This thesis explores the potential of using state-of-the-art machine learning algorithms to handle this burden more accurately and quickly than trained astronomers. To this end Deep Learning methods are applied to classify transients using real-world data from the Sloan Digital Sky Survey. Using cutting-edge training techniques several Convolutional Neural networks are trained and hyper-parameters tuned to outperform previous approaches and find that hu
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Du, Buisson Lise. "Machine learning in astronomy." Master's thesis, University of Cape Town, 2015. http://hdl.handle.net/11427/15502.

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The search to find answers to the deepest questions we have about the Universe has fueled the collection of data for ever larger volumes of our cosmos. The field of supernova cosmology, for example, is seeing continuous development with upcoming surveys set to produce a vast amount of data that will require new statistical inference and machine learning techniques for processing and analysis. Distinguishing between real objects and artefacts is one of the first steps in any transient science pipeline and, currently, is still carried out by humans - often leading to hand scanners having to sort
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Goldenberg, David. "Adaptive learning and cryptography." W&M ScholarWorks, 2010. https://scholarworks.wm.edu/etd/1539623564.

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Significant links exist between cryptography and computational learning theory. Cryptographic functions are the usual method of demonstrating significant intractability results in computational learning theory as they can demonstrate that certain problems are hard in a representation independent sense. On the other hand, hard learning problems have been used to create efficient cryptographic protocols such as authentication schemes, pseudo-random permutations and functions, and even public key encryption schemes.;Learning theory / coding theory also impacts cryptography in that it enables cryp
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Chan, Cho-kui, and 陳祖鉅. "Collaborative learning on Internet: learning applied mathematics through newsgroup on the net." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1998. http://hub.hku.hk/bib/B31959957.

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Chan, Cho-kui. "Collaborative learning on Internet : learning applied mathematics through newsgroup on the net /." Hong Kong : University of Hong Kong, 1998. http://sunzi.lib.hku.hk/hkuto/record.jsp?B20057428.

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Yang, Lili. "Machine learning methodologies applied to fire risk management." Thesis, University of Derby, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.407044.

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Tummala, Akhil. "Self-learning algorithms applied in Continuous Integration system." Thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-16675.

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Context: Continuous Integration (CI) is a software development practice where a developer integrates a code into the shared repository. And, then an automated system verifies the code and runs automated test cases to find integration error. For this research, Ericsson’s CI system is used. The tests that are performed in CI are regression tests. Based on the time scopes, the regression test suites are categorized into hourly and daily test suits. The hourly test is performed on all the commits made in a day, whereas the daily test is performed at night on the latest build that passed the hourly
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Shukla, Manu. "Algorithmic Distribution of Applied Learning on Big Data." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/100603.

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Machine Learning and Graph techniques are complex and challenging to distribute. Generally, they are distributed by modeling the problem in a similar way as single node sequential techniques except applied on smaller chunks of data and compute and the results combined. These techniques focus on stitching the results from smaller chunks as the best possible way to have the outcome as close to the sequential results on entire data as possible. This approach is not feasible in numerous kernel, matrix, optimization, graph, and other techniques where the algorithm needs access to all the data durin
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Ulezla, Dzmitry. "Applied use of OLAP-technologies in machine learning." Thesis, ВНТУ, 2014. http://ir.lib.vntu.edu.ua/handle/123456789/4518.

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Metric classification methods and their features, OLAP-cubes advantages, methods of their application in practice are studied. This work discusses the methods of OLAP-technologies applied use in machine learning, their synthesis with metric classification objects methods.<br>Изучаются методы метрической классификации и их особенности, преимущества OLAP- кубов данных, способы применения их на практике. В данной работе рассматриваются способы прикладного использования OLAP-технологий в машинном обучении, их синтез с метрическими методами классификации объектов.
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Adebonojo, Leslie G., and F. R. Jelovsek. "Learning Principles as Applied to Computer-Assisted Instruction." Digital Commons @ East Tennessee State University, 1993. https://dc.etsu.edu/etsu-works/6312.

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25

Paes, Herrera Gabriel. "Implementation of Machine Learning Methods in Applied Economics." Thesis, Griffith University, 2022. http://hdl.handle.net/10072/417697.

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This thesis aims to expand the research frontier and contribute to the literature on the application of machine learning (ML) in economics. Traditional econometric methods and machine learning modes have their advantages and disadvantages. Analysis techniques are constantly evolving to provide better solutions to solve problems and extract insights from data. This work includes four chapters, each of which explores a relevant application of ML algorithms in different economic segments. Apart from that, the thesis also includes an introduction and a final chapter containing concluding remarks.
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Lamb, Darren Hayes. "Project based learning in an applied construction curriculum." CSUSB ScholarWorks, 2003. https://scholarworks.lib.csusb.edu/etd-project/2188.

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This project addresses the integration of a career and technical (vocational) construction curriculum with academic curriculum. Career and technical (vocational) curriculum in the past has been developed to address specific content. This construction curriculum inegrates inherent academic aspects.
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Sadok, Samir. "Audiovisual speech representation learning applied to emotion recognition." Electronic Thesis or Diss., CentraleSupélec, 2024. http://www.theses.fr/2024CSUP0003.

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Les émotions sont vitales dans notre quotidien, devenant un centre d'intérêt majeur de la recherche en cours. La reconnaissance automatique des émotions a suscité beaucoup d'attention en raison de ses applications étendues dans des secteurs tels que la santé, l'éducation, le divertissement et le marketing. Ce progrès dans la reconnaissance émotionnelle est essentiel pour favoriser le développement de l'intelligence artificielle centrée sur l'humain. Les systèmes de reconnaissance des émotions supervisés se sont considérablement améliorés par rapport aux approches traditionnelles d’apprentissag
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Kim, Beomjoon. "Efficient imitation learning and inverse reinforcement learning with application to navigation in human environments." Thesis, McGill University, 2014. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=121555.

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A key skill for mobile robots is the ability to navigate efficiently through their environment. In the case of social or assistive robots, this involves navigating through human crowds. Typical performance criteria, such as reaching the goal using the shortest path, are not appropriate in such environments, where it is more important for the robot to move in a socially adaptive manner such as respecting comfort zones of the pedestrians. This thesis investigates the Learning from Demonstration framework to address the socially adaptive path planning problem. Learning from Demonstration is a pra
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Zhang, Bo. "Machine Learning on Statistical Manifold." Scholarship @ Claremont, 2017. http://scholarship.claremont.edu/hmc_theses/110.

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This senior thesis project explores and generalizes some fundamental machine learning algorithms from the Euclidean space to the statistical manifold, an abstract space in which each point is a probability distribution. In this thesis, we adapt the optimal separating hyperplane, the k-means clustering method, and the hierarchical clustering method for classifying and clustering probability distributions. In these modifications, we use the statistical distances as a measure of the dissimilarity between objects. We describe a situation where the clustering of probability distributions is needed
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Jones, Piet. "Structure learning of gene interaction networks." Thesis, Stellenbosch : Stellenbosch University, 2014. http://hdl.handle.net/10019.1/86650.

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Thesis (MSc)--Stellenbosch University, 2014.<br>ENGLISH ABSTRACT: There is an ever increasing wealth of information that is being generated regarding biological systems, in particular information on the interactions and dependencies of genes and their regulatory process. It is thus important to be able to attach functional understanding to this wealth of information. Mathematics can potentially provide the tools needed to generate the necessary abstractions to model the complex system of gene interaction. Here the problem of uncovering gene interactions is cast in several contexts, name
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Abou-Moustafa, Karim. "Metric learning revisited: new approaches for supervised and unsupervised metric learning with analysis and algorithms." Thesis, McGill University, 2012. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=106370.

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In machine learning one is usually given a data set of real high dimensional vectors X, based on which it is desired to select a hypothesis θ from the space of hypotheses Θ using a learning algorithm. An immediate assumption that is usually imposed on X is that it is a subset from the very general embedding space Rp which makes the Euclidean distance ∥•∥2 to become the default metric for the elements of X. Since various learning algorithms assume that the input space is Rp with its endowed metric ∥•∥2 as a (dis)similarity measure, it follows that selecting hypothesis θ becomes intrinsically ti
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Roots, Lindsey. "Learning to trust : experimental evidence of social learning in a real-world social network of player A's in a trust game." Master's thesis, University of Cape Town, 2013. http://hdl.handle.net/11427/5704.

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Meyer, Imke. "Instrumental conditioning and learning in Poroderma Pantherinum." Master's thesis, University of Cape Town, 2017. http://hdl.handle.net/11427/24515.

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Recent research has shown that higher cognitive functions and learning occur in teleosts and elasmobranchs. Very little is known about the cognitive abilities of benthic sharks and no research has been published on the learning ability of the endemic Leopard catshark (Poroderma pantherinum) species of South Africa. This species is listed as data deficient on the IUCN red list and known threats due to anthropogenic impacts include fatalities because of bycatch and depredation in the small-scale commercial fishing industry. It has been suggested that sharks can be attracted to fishing boats thro
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Blake, Damien, and mikewood@deakin edu au. "From risk to relationship: Redefining pedagogy through applied learning reform." Deakin University. School of Social and Cultural Studies in Education, 2004. http://tux.lib.deakin.edu.au./adt-VDU/public/adt-VDU20060517.150434.

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The Victorian Certificate of Applied Learning (VCAL) emerged to provide more relevant curriculum programs that would cater for increasing retention rates of post-compulsory students. It is also an example of the ‘new’ learning arising from contemporary debates and reforms that highlight inadequacies of the more traditional modes of learning. This thesis focuses on the pedagogical and sociological issues emerging from the VCAL being introduced as an ‘alternative’ learning pathways for ‘at-risk’ students within a traditional secondary school culture. Through the eyes of an insider-researcher,
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Faulkner, Ryan. "Dyna learning with deep belief networks." Thesis, McGill University, 2011. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=97177.

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The objective of reinforcement learning is to find "good" actions in an environment where feedback is provided through a numerical reward, and the current state (i.e. sensory input) is assumed to be available at each time step. The notion of "good" is defined as maximizing the expected cumulative returns over time. Sometimes it is useful to construct models of the environment to aid in solving the problem. We investigate Dyna-style reinforcement learning, a powerful approach for problems where not much real data is available. The main idea is to supplement real trajectories with simulated o
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Agrawal, Punit. "Program navigation analysis using machine learning." Thesis, McGill University, 2009. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=32599.

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Developers invest a large portion of their development time exploring program source code to find task-related code elements and to understand the context of their task. The task context is usually not recorded at the end of the task and is forgotten over time. Similarly, it is not possible to share the task context with other developers working on related tasks. Proposed solutions to automatically record the summary of the code investigation suffer from methodological limitations related to the technique
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Bordianu, Gheorghita. "Learning influence probabilities in social networks." Thesis, McGill University, 2013. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=114597.

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Social network analysis is an important cross-disciplinary area of research, with applications in fields such as biology, epidemiology, marketing and even politics. Influence maximization is the problem of finding the set of seed nodes in an information diffusion process that guarantees maximum spread of influence in a social network, given its structure. Most approaches to this problem make two assumptions. First, the global structure of the network is known. Second, influence probabilities between any two nodes are known beforehand, which is rarely the case in practical settings. In this the
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Skubch, Hendrik. "Hierarchical strategy learning for FLUX agents : an applied technique /." Saarbrücken : VDM Verlag Dr. Müller, 2007. http://deposit.d-nb.de/cgi-bin/dokserv?id=3057454&prov=M&dok_var=1&dok_ext=htm.

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Downing, Jillian. "Applied learning design in an online teacher-education course." Thesis, Downing, Jillian (2015) Applied learning design in an online teacher-education course. PhD thesis, Murdoch University, 2015. https://researchrepository.murdoch.edu.au/id/eprint/30925/.

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The profile of students enrolled in higher education is changing rapidly. Compared to a generation ago, students are likely to be older, juggling study with work and personal commitments and may well be the first in their family to attend university. These students arrive at university with a significant store of life experience but often filled with trepidation about their capacity to succeed academically. Despite their classification as ‘non-traditional’ students it is usually assumed that they will engage effectively with traditional approaches in the learning and teaching environment. Yet
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Pagés, Sara Elizabeth. "Applied Securities Analysis: A Look Inside the Learning Process." Thesis, The University of Arizona, 2011. http://hdl.handle.net/10150/144933.

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Andersson, Carl. "Deep learning applied to system identification : A probabilistic approach." Licentiate thesis, Uppsala universitet, Avdelningen för systemteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-397563.

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Machine learning has been applied to sequential data for a long time in the field of system identification. As deep learning grew under the late 00's machine learning was again applied to sequential data but from a new angle, not utilizing much of the knowledge from system identification. Likewise, the field of system identification has yet to adopt many of the recent advancements in deep learning. This thesis is a response to that. It introduces the field of deep learning in a probabilistic machine learning setting for problems known from system identification. Our goal for sequential modelin
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MARZIALI, ANDREA. "Machine learning models applied to energy time-series forecasting." Doctoral thesis, Università degli studi di Pavia, 2020. http://hdl.handle.net/11571/1326207.

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Dinculescu, Monica. "Learning approximate representations of partially observable systems." Thesis, McGill University, 2010. http://digitool.Library.McGill.CA:8881/R/?func=dbin-jump-full&object_id=92257.

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Duala-Ekoko, Ekwa. "Using structural relationships to facilitate API learning." Thesis, McGill University, 2012. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=107667.

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Application Programming Interfaces (APIs) allow software developers to reuse code libraries, frameworks, or services without the need of having to implement relevant functionalities from scratch. The benefits of reusing source code or services through APIs have encouraged the adoption of APIs as the building blocks of modern-day software systems. However, leveraging the benefits of APIs require a developer to frequently learn how to use unfamiliar APIs --- a process made difficult by the increasing size of APIs, and the increase in the number of APIs with which a developer has to work. In th
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Dagenais, Barthélémy. "Analysis and recommendations for developer learning resources." Thesis, McGill University, 2012. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=110512.

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Developer documentation helps developers learn frameworks and libraries, yet developing and maintaining accurate documentation require considerable effort and resources. Contributors who work on developer documentation need to at least take into account the project's code and the support needs of users. Although related, the documentation, the code, and the support needs evolve and are not always synchronized: for example, new features in the code are not always documented and questions repeatedly asked by users on support channels such as mailing lists may not be addressed by the documentati
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Giguère, Philippe. "Unsupervised learning for mobile robot terrain classification." Thesis, McGill University, 2010. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=95062.

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In this thesis, we consider the problem of having a mobile robot autonomously learn to perceive differences between terrains. The targeted application is for terrain identification. Robust terrain identification can be used to enhance the capabilities of mobile systems, both in terms of locomotion and navigation. For example, a legged amphibious robot that has learned to differentiate sand from water can automatically select its gait on a beach: walking for sand, and swimming for water. The same terrain information can also be used to guide a robot in order to avoid specific terrain types. Th
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Allberg, Petrus. "Applied machine learning in the logistics sector : A comparative analysis of supervised learning algorithms." Thesis, Blekinge Tekniska Högskola, Institutionen för programvaruteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-16656.

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BackgroundMachine learning is an area that is being explored with great haste these days, which inspired this study to investigate how seven different supervised learning algorithms perform compared to each other. These algorithms were used to perform classification tasks on logistics consignments, the classification is binary and a consignment can either be classified as missed or not. ObjectivesThe goal was to find which of these algorithms perform well when used for this classification task and to see how the results varied with different sized datasets. Importance of the features which wer
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48

Easdown, Alison. "Improving numerical properties of learning algorithms for neural networks." Thesis, University of Brighton, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.363854.

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49

Heine, Matthew Alan. "A constrained optimization model for partitioning students into cooperative learning groups." Thesis, Colorado State University, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10138918.

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Abstract:
<p> The problem of the constrained partitioning of a set using quantitative relationships amongst the elements is considered. An approach based on constrained integer programming is proposed that permits a group objective function to be optimized subject to group quality constraints. A motivation for this problem is the partitioning of students, e.g., in middle school, into groups that target educational objectives. The method is compared to another grouping algorithm in the literature on a data set collected in the Poudre School District.</p>
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50

Zhang, Yue. "Sparsity in Image Processing and Machine Learning: Modeling, Computation and Theory." Case Western Reserve University School of Graduate Studies / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=case1523017795312546.

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