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Thèses sur le sujet « Probability learning »

Auteur : Grafiati

Publié le 4 juin 2021

Mis à jour le 28 juillet 2024

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1

Gozenman, Filiz. "Interaction Of Probability Learning And Working Memory." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614535/index.pdf.

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Probability learning is the ability to establish a relationship between stimulus and outcomes based on occurrence probabilities using repetitive feedbacks. Participants learn the task according to the cue-outcome relationship, and try to gain in depth understanding of this relationship throughout the experiment. While learning is at the highest level, people rely on their working memory. In this study 20 participants were presented a probability learning task, and their prefrontal cortex activity was measured with functional Near-Infrared Spectroscopy. It was hypothesized that as participants

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RYSZ, TERI. "METACOGNITION IN LEARNING ELEMENTARY PROBABILITY AND STATISTICS." University of Cincinnati / OhioLINK, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1099248340.

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Bouchacourt, Diane. "Task-oriented learning of structured probability distributions." Thesis, University of Oxford, 2017. https://ora.ox.ac.uk/objects/uuid:0665495b-afbb-483b-8bdf-cbc6ae5baeff.

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Machine learning models automatically learn from historical data to predict unseen events. Such events are often represented as complex multi-dimensional structures. In many cases there is high uncertainty in the prediction process. Research has developed probabilistic models to capture distributions of complex objects, but their learning objective is often agnostic of the evaluation loss. In this thesis, we address the aforementioned defficiency by designing probabilistic methods for structured object prediction that take into account the task at hand. First, we consider that the task at hand

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Li, Chengtao Ph D. Massachusetts Institute of Technology. "Diversity-inducing probability measures for machine learning." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/121724.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 163-176).<br>Subset selection problems arise in machine learning within kernel approximation, experimental design, and numerous other applications. In such applications, one often seeks to select diverse subsets of items to represent the population. One way to select such diverse subsets is to sample according to Diversity-Inducing Probability Measures (DIPMs) that assign higher probabilitie

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Hunt, Gareth David. "Reinforcement Learning for Low Probability High Impact Risks." Thesis, Curtin University, 2019. http://hdl.handle.net/20.500.11937/77106.

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We demonstrate a method of reinforcement learning that uses training in simulation. Our system generates an estimate of the potential reward and danger of each action as well as a measure of the uncertainty present in both. The system generates this by seeking out not only rewarding actions but also dangerous ones in the simulated training. During runtime our system is able to use this knowledge to avoid risks while accomplishing its tasks.

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Słowiński, Witold. "Autonomous learning of domain models from probability distribution clusters." Thesis, University of Aberdeen, 2014. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=211059.

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Nontrivial domains can be difficult to understand and the task of encoding a model of such a domain can be difficult for a human expert, which is one of the fundamental problems of knowledge acquisition. Model learning provides a way to address this problem by allowing a predictive model of the domain's dynamics to be learnt algorithmically, without human supervision. Such models can provide insight about the domain to a human or aid in automated planning or reinforcement learning. This dissertation addresses the problem of how to learn a model of a continuous, dynamic domain, from sensory obs

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Benson, Carol Trinko Jones Graham A. "Assessing students' thinking in modeling probability contexts." Normal, Ill. Illinois State University, 2000. http://wwwlib.umi.com/cr/ilstu/fullcit?p9986725.

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Thesis (Ph. D.)--Illinois State University, 2000.<br>Title from title page screen, viewed May 11, 2006. Dissertation Committee: Graham A. Jones (chair), Kenneth N. Berk, Patricia Klass, Cynthia W. Langrall, Edward S. Mooney. Includes bibliographical references (leaves 115-124) and abstract. Also available in print.

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Rast, Jeanne D. "A Comparison of Learning Subjective and Traditional Probability in Middle Grades." Digital Archive @ GSU, 2005. http://digitalarchive.gsu.edu/msit_diss/4.

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The emphasis given to probability and statistics in the K-12 mathematics curriculum has brought attention to the various approaches to probability and statistics concepts, as well as how to teach these concepts. Teachers from fourth, fifth, and sixth grades from a small suburban Catholic school engaged their students (n=87) in a study to compare learning traditional probability concepts to learning traditional and subjective probability concepts. The control group (n=44) received instruction in traditional probability, while the experimental group (n=43) received instruction in traditional and

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Lindsay, David George. "Machine learning techniques for probability forecasting and their practical evaluations." Thesis, Royal Holloway, University of London, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.445274.

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Kornfeld, Sarah. "Predicting Default Probability in Credit Risk using Machine Learning Algorithms." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-275656.

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This thesis has explored the field of internally developed models for measuring the probability of default (PD) in credit risk. As regulators put restrictions on modelling practices and inhibit the advance of risk measurement, the fields of data science and machine learning are advancing. The tradeoff between stricter regulation on internally developed models and the advancement of data analytics was investigated by comparing model performance of the benchmark method Logistic Regression for estimating PD with the machine learning methods Decision Trees, Random Forest, Gradient Boosting and Art

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Ives, Sarah Elizabeth. "Learning to Teach Probability: Relationships among Preservice Teachers' Beliefs and Orientations, Content Knowledge, and Pedagogical Content Knowledge of Probability." NCSU, 2009. http://www.lib.ncsu.edu/theses/available/etd-11042009-144919/.

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The purposes of this study were to investigate preservice mathematics teachersâ orientations, content knowledge, and pedagogical content knowledge of probability; the relationships among these three aspects; and the usefulness of tasks with respect to examining these aspects of knowledge. The design of the study was a multi-case study of five secondary mathematics education preservice teachers with a focus on their knowledge as well as tasks that were used in this study. Data from individual interviews and test items were collected and analyzed under a conceptual framework based on the work of

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Nogales, Chris Lorena. "Robot Autonomous Fire Location using a Weighted Probability Algorithm." Thesis, Virginia Tech, 2016. http://hdl.handle.net/10919/73360.

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Finding a fire inside of a structure without knowing its conditions poses a dangerous threat to the safety of firefighters. As a result, robots are being explored to increase awareness of the conditions inside structures before having firefighter enter. This thesis presents a method that autonomously guides a robot to the location of a fire inside a structure. The method uses classification of fire, smoke, and other fire environment objects to calculate a weighted probability. Weighted probability is a measurement that indicates the probability that a given region on an infra-red image will le

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Seow, Hsin-Vonn. "Using adaptive learning in credit scoring to estimate acceptance probability distribution." Thesis, University of Southampton, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.430721.

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Miller, Erik G. (Erik Gundersen). "Learning from one example in machine vision by sharing probability densities." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/29902.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.<br>Includes bibliographical references (p. 125-130).<br>Human beings exhibit rapid learning when presented with a small number of images of a new object. A person can identify an object under a wide variety of visual conditions after having seen only a single example of that object. This ability can be partly explained by the application of previously learned statistical knowledge to a new setting. This thesis presents an approach to acquiring knowledge in one setting and using i

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Makar, Maggie S. M. Massachusetts Institute of Technology. "Learning the probability of activation in the presence of latent spreaders." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/111924.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 71-74).<br>When an infection spreads among members of a community, an individual's probability of becoming infected depends on both his susceptibility to the infection and exposure to the disease through contact with others. While one often has knowledge regarding an individual's susceptibility, in many cases, whether or not an individual's contacts are contagious and spreading the infection

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Hild, Andreas. "ESTIMATING AND EVALUATING THE PROBABILITY OF DEFAULT – A MACHINE LEARNING APPROACH." Thesis, Uppsala universitet, Statistiska institutionen, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-447385.

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In this thesis, we analyse and evaluate classification models for panel credit risk data. Variables are selected based on results from recursive feature elimination as well as economic reasoning where the probability of default is estimated. We employ several machine learning and statistical techniques and assess the performance of each model based on AUC, Brier score as well as the absolute mean difference between the predicted and the actual outcome, carried out with cross validation of four folds and extensive hyperparameter optimization. The LightGBM model had the best performance and many

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Bonneau, Maxime. "Reinforcement Learning for 5G Handover." Thesis, Linköpings universitet, Statistik och maskininlärning, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-140816.

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The development of the 5G network is in progress, and one part of the process that needs to be optimised is the handover. This operation, consisting of changing the base station (BS) providing data to a user equipment (UE), needs to be efficient enough to be a seamless operation. From the BS point of view, this operation should be as economical as possible, while satisfying the UE needs. In this thesis, the problem of 5G handover has been addressed, and the chosen tool to solve this problem is reinforcement learning. A review of the different methods proposed by reinforcement learning led to

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Saive, Yannick. "DirCNN: Rotation Invariant Geometric Deep Learning." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252573.

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Recently geometric deep learning introduced a new way for machine learning algorithms to tackle point cloud data in its raw form. Pioneers like PointNet and many architectures building on top of its success realize the importance of invariance to initial data transformations. These include shifting, scaling and rotating the point cloud in 3D space. Similarly to our desire for image classifying machine learning models to classify an upside down dog as a dog, we wish geometric deep learning models to succeed on transformed data. As such, many models employ an initial data transform in their mode

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Sandberg, Martina. "Credit Risk Evaluation using Machine Learning." Thesis, Linköpings universitet, Statistik och maskininlärning, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-138968.

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In this thesis, we examine the machine learning models logistic regression, multilayer perceptron and random forests in the purpose of discriminate between good and bad credit applicants. In addition to these models we address the problem of imbalanced data with the Synthetic Minority Over-Sampling Technique (SMOTE). The data available have 273 286 entries and contains information about the invoice of the applicant and the credit decision process as well as information about the applicant. The data was collected during the period 2015-2017. With AUC-values at about 73%some patterns are found t

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Goerg, Georg Matthias. "Learning Spatio-Temporal Dynamics: Nonparametric Methods for Optimal Forecasting and Automated Pattern Discovery." Research Showcase @ CMU, 2012. http://repository.cmu.edu/dissertations/218.

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Many important scientific and data-driven problems involve quantities that vary over space and time. Examples include functional magnetic resonance imaging (fMRI), climate data, or experimental studies in physics, chemistry, and biology. Principal goals of many methods in statistics, machine learning, and signal processing are to use this data and i) extract informative structures and remove noisy, uninformative parts; ii) understand and reconstruct underlying spatio-temporal dynamics that govern these systems; and iii) forecast the data, i.e., describe the system in the future. Being data-dri

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Nilsson, Viktor. "Prediction of Dose Probability Distributions Using Mixture Density Networks." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273610.

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In recent years, machine learning has become utilized in external radiation therapy treatment planning. This involves automatic generation of treatment plans based on CT-scans and other spatial information such as the location of tumors and organs. The utility lies in relieving clinical staff from the labor of manually or semi-manually creating such plans. Rather than predicting a deterministic plan, there is great value in modeling it stochastically, i.e. predicting a probability distribution of dose from CT-scans and delineated biological structures. The stochasticity inherent in the RT trea

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Eiter, Brianna M. "Disappearing effects of transitional probability on visual word recognition during reading." Diss., Online access via UMI:, 2005.

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Vallin, Simon. "Small Cohort Population Forecasting via Bayesian Learning." Thesis, KTH, Matematisk statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-209274.

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A set of distributional assumptions regarding the demographic processes of birth, death, emigration and immigration have been assembled to form a probabilistic model framework of population dynamics. This framework was summarized as a Bayesian network and Bayesian inference techniques are exploited to infer the posterior distributions of the model parameters from observed data. The birth, death and emigration processes are modelled using a hierarchical beta-binomial model from which the inference of the posterior parameter distribution was analytically tractable. The immigration process was mo

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McKinnon, Melissa Taylor. "Probability and Statistics for Third through Fifth Grade Classrooms." Digital Commons @ East Tennessee State University, 2007. https://dc.etsu.edu/etd/2118.

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This document contains a variety of lesson plans that can be readily used by a teacher of intermediate students. This thesis contains two units in Probability and one unit in Statistics. Any educator can supplement this document with any curriculum to teach lessons from vocabulary to concept.

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Maginnis, Michael Abbot. "THE DEVELOPMENT OF A PREDICTIVE PROBABILITY MODEL FOR EFFECTIVE CONTINUOUS LEARNING AND IMPROVEMENT." UKnowledge, 2012. http://uknowledge.uky.edu/me_etds/2.

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It is important for organizations to understand the factors responsible for establishing sustainable continuous improvement (CI) capabilities. This study uses learning curves as the basis to examine learning obtained by team members doing work with and without the application of fundamental aspects of the Toyota Production System. The results are used to develop an effective model to guide organizational activities towards achieving the ability to continuous improve in a sustainable fashion. This research examines the effect of standardization and waste elimination activities supported by syst

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Callahan, Philip. "Learning and development of probability concepts: Effects of computer-assisted instruction and diagnosis." Diss., The University of Arizona, 1989. http://hdl.handle.net/10150/184873.

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This study considered spontaneous versus feedback induced changes in probability strategies using grouped trials of two-choice problems. Third and sixth grade Anglo and Apache children were the focus of computer assisted instruction and diagnostics designed to maximize performance and measure understanding of probability concepts. Feedback, using indeterminate problems directed at specific strategies, in combination with a large problem set permitted examination of response latency and hypothesis alternation. Explicit training, in the form of computer based tutorials administered feedback as:

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Damasse, Jean-Bernard. "Smooth pursuit eye movements and learning : role of motion probability and reinforcement contingencies." Thesis, Aix-Marseille, 2018. http://www.theses.fr/2018AIXM0223/document.

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Un défi majeur pour les organismes vivants est leur capacité d’adapter constamment leurs comportements moteurs. Dans la première étude de cette thèse, nous avons étudié le rôle des régularités statistiques et du conditionnement opérant sur la poursuite lisse d’anticipation (PLA). Nous avons démontré que la PLA est générée de manière cohérente avec la probabilité du mouvement attendu. De plus, nous avons montré pour la première fois que la PLA peut être modulée par les contingences de renforcement.Dans une seconde étude, nous avons créé un paradigme de poursuite, inspiré par l’Iowa Gambling Tas

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Lundell, Jill F. "Tuning Hyperparameters in Supervised Learning Models and Applications of Statistical Learning in Genome-Wide Association Studies with Emphasis on Heritability." DigitalCommons@USU, 2019. https://digitalcommons.usu.edu/etd/7594.

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Machine learning is a buzz word that has inundated popular culture in the last few years. This is a term for a computer method that can automatically learn and improve from data instead of being explicitly programmed at every step. Investigations regarding the best way to create and use these methods are prevalent in research. Machine learning models can be difficult to create because models need to be tuned. This dissertation explores the characteristics of tuning three popular machine learning models and finds a way to automatically select a set of tuning parameters. This information was use

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Brookey, Carla M. "Application of Machine Learning and Statistical Learning Methods for Prediction in a Large-Scale Vegetation Map." DigitalCommons@USU, 2017. https://digitalcommons.usu.edu/etd/6962.

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Original analyses of a large vegetation cover dataset from Roosevelt National Forest in northern Colorado were carried out by Blackard (1998) and Blackard and Dean (1998; 2000). They compared the classification accuracies of linear and quadratic discriminant analysis (LDA and QDA) with artificial neural networks (ANN) and obtained an overall classification accuracy of 70.58% for a tuned ANN compared to 58.38% for LDA and 52.76% for QDA. Because there has been tremendous development of machine learning classification methods over the last 35 years in both computer science and statistics, as wel

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Eriksson, Alexander, and Jacob Långström. "Comparison of Machine Learning Techniques when Estimating Probability of Impairment : Estimating Probability of Impairment through Identification of Defaulting Customers one year Ahead of Time." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-160114.

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Probability of Impairment, or Probability of Default, is the ratio of how many customers within a segment are expected to not fulfil their debt obligations and instead go into Default. This is a key metric within banking to estimate the level of credit risk, where the current standard is to estimate Probability of Impairment using Linear Regression. In this paper we show how this metric instead can be estimated through a classification approach with machine learning. By using models trained to find which specific customers will go into Default within the upcoming year, based on Neural Networks

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Bratières, Sébastien. "Non-parametric Bayesian models for structured output prediction." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/274973.

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Structured output prediction is a machine learning tasks in which an input object is not just assigned a single class, as in classification, but multiple, interdependent labels. This means that the presence or value of a given label affects the other labels, for instance in text labelling problems, where output labels are applied to each word, and their interdependencies must be modelled. Non-parametric Bayesian (NPB) techniques are probabilistic modelling techniques which have the interesting property of allowing model capacity to grow, in a controllable way, with data complexity, while maint

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Enver, Asad. "Modeling Trouble Ticket ResolutionTime Using Machine Learning." Thesis, Linköpings universitet, Statistik och maskininlärning, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176779.

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This thesis work, conducted at Telenor Sweden, aims to build a model that would try to accurately predict the resolution time of Priority 4 Trouble Tickets. (Priority 4 trouble tickets are those tickets that get generated more often-e in higher volumes per month). It explores and investigates the possibility of applying Machine Learning and Deep Learning techniques to trouble ticket data to find an optimal solution that performs better than the current method in place (which is explained in Section 3.5). The model would be used by Telenor to inform the end-users of when the networks team expec

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Erdeniz, Burak. "Probability Learning In Normal And Parkinson Subjects: The Effect Of Reward, Context, And Uncertainty." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12608877/index.pdf.

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In this thesis, the learning of probabilistic relationships between stimulus-action pairs is investigated under the probability learning paradigm. The effect of reward is investigated in the first three experiments. Additionally, the effect of context and uncertainty is investigated in the second and third experiments, respectively. The fourth experiment is the replication of the second experiment with a group of Parkinson patients where the effect of dopamine medication on probability learning is studied. In Experiment 1, we replicate the classical probability learning task by comparing mone

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Sjöholm, Johan. "Probability as readability : A new machine learning approach to readability assessment for written Swedish." Thesis, Linköpings universitet, NLPLAB - Laboratoriet för databehandling av naturligt språk, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-78107.

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This thesis explores the possibility of assessing the degree of readability of writtenSwedish using machine learning. An application using four levels of linguistic analysishas been implemented and tested with four different established algorithmsfor machine learning. The new approach has then been compared to establishedreadability metrics for Swedish. The results indicate that the new method workssignificantly better for readability classification of both sentences and documents.The system has also been tested with so called soft classification which returns aprobability for the degree of re

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Lindberg, Jesper. "Simulation driven reinforcement learning : Improving synthetic enemies in flight simulators." Thesis, Linköpings universitet, Statistik och maskininlärning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166593.

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This project focuses on how to implement an Artificial Intelligence (AI) -agent in a Tactical Simulator (Tacsi). Tacsi is a simulator used by Saab AB, one thing that the simulator is used for is pilot training. In this work, Tacsi will be used to simulate air to air combat. The agent uses Reinforcement Learning (RL) to be able to explore and learn how the simulator behaves. This knowledge will then be exploited when the agent tries to beat a computer-controlled synthetic enemy. The result of this study may be used to produce better synthetic enemies for pilot training. The RL-algorithm used in

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Rydén, Otto. "Statistical learning procedures for analysis of residential property price indexes." Thesis, KTH, Matematisk statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-207946.

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Residential Price Property Indexes (RPPIs) are used to study the price development of residential property over time. Modeling and analysing an RPPI is not straightforward due to residential property being a heterogeneous good. This thesis focuses on analysing the properties of the two most conventional hedonic index modeling approaches, the hedonic time dummy method and the hedonic imputation method. These two methods are analysed with statistical learning procedures from a regression perspective, specifically, ordinary least squares regression, and a number of more advanced regression approa

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Rayburn-Reeves, Rebecca Marie. "AN ANALYSIS OF BEHAVIORAL FLEXIBILITY AND CUE PREFERENCE IN PIGEONS UNDER VARIABLE REVERSAL LEARNING CONDITIONS." UKnowledge, 2011. http://uknowledge.uky.edu/psychology_etds/1.

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Behavioral flexibility, the ability to change behavior in accordance with the changing environment, was studied in pigeons using a series of reversal learning paradigms. All experiments involved a series of 5-trial sequences and I was interested in whether pigeons are sensitive to the reversal by switching to the other alternative after a single error. In Experiments 1 and 2, the overall probability of the two stimuli was equated over sequences, but the probability correct of the two stimuli changed across trials. In both experiments, subjects showed no sensitivity to the differences in sequen

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BELLODI, Elena. "Integration of Logic and Probability in Terminological and Inductive Reasoning." Doctoral thesis, Università degli studi di Ferrara, 2013. http://hdl.handle.net/11392/2388897.

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This thesis deals with Statistical Relational Learning (SRL), a research area combining principles and ideas from three important subfields of Artificial Intelligence: machine learn- ing, knowledge representation and reasoning on uncertainty. Machine learning is the study of systems that improve their behavior over time with experience; the learning process typi- cally involves a search through various generalizations of the examples, in order to discover regularities or classification rules. A wide variety of machine learning techniques have been developed in the past fifty years, most of w

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Mirzaikamrani, Sonya. "Predictive modeling and classification for Stroke using the machine learning methods." Thesis, Örebro universitet, Handelshögskolan vid Örebro Universitet, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-81837.

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Hedblom, Edvin, and Rasmus Åkerblom. "Debt recovery prediction in securitized non-performing loans using machine learning." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252311.

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Credit scoring using machine learning has been gaining attention within the research field in recent decades and it is widely used in the financial sector today. Studies covering binary credit scoring of securitized non-performing loans are however very scarce. This paper is using random forest and artificial neural networks to predict debt recovery for such portfolios. As a performance benchmark, logistic regression is used. Due to the nature of high imbalance between the classes, the performance is evaluated mainly on the area under both the receiver operating characteristic curve and the pr

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Truran, J. M. "The development of children's understanding of probability : and the application of research findings to classroom practice /." Title page, contents and abstract only, 1992. http://web4.library.adelaide.edu.au/theses/09EDM/09edmt872.pdf.

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Shipitsyn, Aleksey. "Statistical Learning with Imbalanced Data." Thesis, Linköpings universitet, Filosofiska fakulteten, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-139168.

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In this thesis several sampling methods for Statistical Learning with imbalanced data have been implemented and evaluated with a new metric, imbalanced accuracy. Several modifications and new algorithms have been proposed for intelligent sampling: Border links, Clean Border Undersampling, One-Sided Undersampling Modified, DBSCAN Undersampling, Class Adjusted Jittering, Hierarchical Cluster Based Oversampling, DBSCAN Oversampling, Fitted Distribution Oversampling, Random Linear Combinations Oversampling, Center Repulsion Oversampling. A set of requirements on a satisfactory performance metric f

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Stagner, Jessica P. "INVESTIGATION OF THE MONTY HALL DILEMMA IN PIGEONS AND RATS." UKnowledge, 2013. http://uknowledge.uky.edu/psychology_etds/31.

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In the Monty Hall Dilemma (MHD), three doors are presented with a prize behind one and participants are instructed to choose a door. One of the unchosen doors is then shown to not have the prize and the participant can choose to stay with their door or switch to the other one. The optimal strategy is to switch. Herbranson and Schroeder (2010) found that humans performed poorly on this task, whereas pigeons learned to switch readily. However, we found that pigeons learned to switch at level only slightly above humans. We also found that pigeons stay nearly exclusively when staying is the optima

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Samson, Rachel D., Adam W. Lester, Leroy Duarte, Anu Venkatesh та Carol A. Barnes. "Emergence of β-Band Oscillations in the Aged Rat Amygdala during Discrimination Learning and Decision Making Tasks". SOC NEUROSCIENCE, 2017. http://hdl.handle.net/10150/626610.

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Older adults tend to use strategies that differ from those used by young adults to solve decision-making tasks. MRI experiments suggest that altered strategy use during aging can be accompanied by a change in extent of activation of a given brain region, inter-hemispheric bilateralization or added brain structures. It has been suggested that these changes reflect compensation for less effective networks to enable optimal performance. One way that communication can be influenced within and between brain networks is through oscillatory events that help structure and synchronize incoming and outg

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Parfionovas, Andrejus. "Enhancement of Random Forests Using Trees with Oblique Splits." DigitalCommons@USU, 2013. http://digitalcommons.usu.edu/etd/1508.

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This work presents an enhancement to the classification tree algorithm which forms the basis for Random Forests. Differently from the classical tree-based methods that focus on one variable at a time to separate the observations, the new algorithm performs the search for the best split in two-dimensional space using a linear combination of variables. Besides the classification, the method can be used to determine variables interaction and perform feature extraction. Theoretical investigations and numerical simulations were used to analyze the properties and performance of the new approach. Com

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Goodie, Adam S. "Base-rate neglect under direct experience /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC IP addresses, 1997. http://wwwlib.umi.com/cr/ucsd/fullcit?p9719865.

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Huang, Xin. "A study on the application of machine learning algorithms in stochastic optimal control." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252541.

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By observing a similarity between the goal of stochastic optimal control to minimize an expected cost functional and the aim of machine learning to minimize an expected loss function, a method of applying machine learning algorithm to approximate the optimal control function is established and implemented via neural approximation. Based on a discretization framework, a recursive formula for the gradient of the approximated cost functional on the parameters of neural network is derived. For a well-known Linear-Quadratic-Gaussian control problem, the approximated neural network function obtained

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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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Bhat, Sooraj. "Syntactic foundations for machine learning." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/47700.

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Machine learning has risen in importance across science, engineering, and business in recent years. Domain experts have begun to understand how their data analysis problems can be solved in a principled and efficient manner using methods from machine learning, with its simultaneous focus on statistical and computational concerns. Moreover, the data in many of these application domains has exploded in availability and scale, further underscoring the need for algorithms which find patterns and trends quickly and correctly. However, most people actually analyzing data today operate far from the e

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Truran, J. M. "The teaching and learning of probability, with special reference to South Australian schools from 1959-1994." Title page, contents and abstract only, 2001. http://web4.library.adelaide.edu.au/theses/09PH/09pht872.pdf.

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