Dissertations / Theses on the topic 'Machine learning interactif'
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Scurto, Hugo. "Designing With Machine Learning for Interactive Music Dispositifs." Electronic Thesis or Diss., Sorbonne université, 2019. http://www.theses.fr/2019SORUS356.
Full textMusic is a cultural and creative practice that enables humans to express a variety of feelings and intentions through sound. Machine learning opens many prospects for designing human expression in interactive music systems. Yet, as a Computer Science discipline, machine learning remains mostly studied from an engineering sciences perspective, which often exclude humans and musical interaction from the loop of the created systems. In this dissertation, I argue in favour of designing with machine learning for interactive music systems. I claim that machine learning must be first and foremost situated in human contexts to be researched and applied to the design of interactive music systems. I present four interdisciplinary studies that support this claim, using human-centred methods and model prototypes to design and apply machine learning to four situated musical tasks: motion-sound mapping, sonic exploration, synthesis exploration, and collective musical interaction. Through these studies, I show that model prototyping helps envision designs of machine learning with human users before engaging in model engineering. I also show that the final human-centred machine learning systems not only helps humans create static musical artifacts, but supports dynamic processes of expression between humans and machines. I call co-expression these processes of musical interaction between humans - who may have an expressive and creative impetus regardless of their expertise - and machines - whose learning abilities may be perceived as expressive by humans. In addition to these studies, I present five applications of the created model prototypes to the design of interactive music systems, which I publicly demonstrated in workshops, exhibitions, installations, and performances. Using a reflexive approach, I argue that the musical contributions enabled by such design practice with machine learning may ultimately complement the scientific contributions of human-centred machine learning. I claim that music research can thus be led through dispositif design, that is, through the technical realization of aesthetically-functioning artifacts that challenge cultural norms on computer science and music
Gosselin, Philippe-Henri. "Apprentissage interactif pour la recherche par le contenu dans les bases multimédias." Habilitation à diriger des recherches, Université de Cergy Pontoise, 2011. http://tel.archives-ouvertes.fr/tel-00660316.
Full textSungeelee, Vaynee. "Human-Machine Co-Learning : interactive curriculum generation for the acquisition of motor skills." Electronic Thesis or Diss., Sorbonne université, 2024. https://theses.hal.science/tel-04828514.
Full textMotor skill acquisition is the process by which someone is able to perform a movement more accurately. In this context, practice plays a crucial role. However, practice is not always adapted to each learner's needs and learning journey. Generating personalised instructions manually is time-consuming and therefore impractical. Creating personalized practice sessions automatically is one way to alleviate this problem. Adaptive strategies that structure training, i.e. , the sequence of tasks executed, according to task difficulty and skill level have the potential to improve motor learning for the individual. This dual process of a machine learning the sequence adapted to a human learner and the human learning from the machine, is what we call co-learning. In this thesis, we study human-machine co-learning in the context of motor learning, i.e., learning sequences are generated at the same time as the human learns to perform the motor task.Machine learning algorithms can analyze the learning tendencies of individual learners and adapt training instructions accordingly. They can also be used to control a human-machine interface, during which humans learn to adapt their movements (e.g. prosthesis control). In this thesis, we leverage Machine Learning to facilitate the acquisition of motor skills. However, the use of Machine Learning to achieve this goal involves challenges : (i) few data is available to train the algorithms, (ii) the interactive nature of the system requires rapid training of machine learning algorithms. (iii) the effectiveness of the algorithms depends on a precise assessment of the learner's skill level, which is difficult to measure in practice and (iv) the degree of control provided to humans when training the machine learning model can impact their learning and the way they build a mental model to predict the system's behavior.The aims of this thesis are twofold: (i) to develop a strategy for structuring the learning of motor tasks (ii) to study interactive systems that can adapt to and be adapted by the learner to provide guidance during practice. Through two studies, we explore different strategies to sequence motor learning tasks. In the first study, we evaluate the accuracy and smoothness of movement execution during the performance of a visuo-motor task. The second study explores how to train a machine learning algorithm in a prosthesis control task. We evaluate both the recognition accuracy of gestures provided by participants as well as participants' understanding of the system.Our results contribute to the field of adaptive learning of motor skills and Human-Computer Interaction. They demonstrate that adapting motor tasks to the learner has advantages in terms of participants' performance and understanding of the task. These results provide insights for creating training protocols and facilitating their transition to applied contexts
Crochepierre, Laure. "Apprentissage automatique interactif pour les opérateurs du réseau électrique." Electronic Thesis or Diss., Université de Lorraine, 2022. http://www.theses.fr/2022LORR0112.
Full textIn the energy transition context and the increase in interconnections between the electricity transmission networks in Europe, the French network operators must now deal with more fluctuations and new network dynamics. To guarantee the safety of the network, operators rely on computer software that allows them to carry out simulations or to monitor the evolution of indicators created manually by experts, thanks to their knowledge of the operation of the network. The French electricity transmission network operator RTE (Réseau de Transport d'Electricité) is particularly interested in developing tools to assist operators in monitoring flows on power lines. Flows are notably important to maintain the network in a safe state, guaranteeing the safety of equipment and people. However, the indicators used are not easy to update because of the expertise required to construct and analyze them.In order to address the stated problem, this thesis aims at constructing indicators, in the form of symbolic expressions, to estimate flows on power lines. The problem is studied from the Symbolic Regression perspective and investigated using both Grammatical Evolution and Reinforcement Learning approaches in which explicit and implicit expert knowledge is taken into account. Explicit knowledge about the physics and expertise of the electrical domain is represented in the form of a Context-Free Grammar to limit the functional space from which an expression is created. A first approach of Interactive Grammatical Evolution proposes to incrementally improve found expressions by updating a grammar between evolutionary learnings. Expressions are obtained on real-world data from the network history, validated by an analysis of learning metrics and an interpretability evaluation. Secondly, we propose a reinforcement approach to search in a space delimited by a Context-Free Grammar in order to build a relevant symbolic expression to applications involving physical constraints. This method is validated on state-of-the-art Symbolic Regression benchmarks and also on a dataset with physical constraints to assess its interpretability.Furthermore, in order to take advantage of the complementarities between the capacities of machine learning algorithms and the expertise of network operators, interactive Symbolic Regression algorithms are proposed and integrated into interactive platforms. Interactivity allows updating the knowledge represented in grammatical form and analyzing, interacting with, and commenting on the solutions found by the different approaches. These algorithms and interactive interfaces also aim to take into account implicit knowledge, which is more difficult to formalize, through interaction mechanisms based on suggestions and user preferences
Lai, Hien Phuong. "Vers un système interactif de structuration des index pour une recherche par le contenu dans des grandes bases d'images." Phd thesis, Université de La Rochelle, 2013. http://tel.archives-ouvertes.fr/tel-00934842.
Full textPace, Aaron J. "Guided Interactive Machine Learning." Diss., CLICK HERE for online access, 2006. http://contentdm.lib.byu.edu/ETD/image/etd1355.pdf.
Full textKrishna, Sooraj. "Modelling communicative behaviours for different roles of pedagogical agents." Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS286.
Full textAgents in a learning environment can have various roles and social behaviours that can influence the goals and motivation of the learners in distinct ways. Self-regulated learning (SRL) is a comprehensive conceptual framework that encapsulates the cognitive, metacognitive, behavioural, motivational and affective aspects of learning and entails the processes of goal setting, monitoring progress, analyzing feedback, adjustment of goals and actions by the learner. In this thesis, we present a multi-agent learning interaction involving various pedagogical agent roles aiming to improve the self-regulation of the learner while engaging in a socially shared learning activity. We used distinct roles of agents, defined by their social attitudes and competence characteristics, to deliver specific regulation scaffolding strategies for the learner. The methodology followed in this Thesis started with the definition of pedagogical agent roles in a socially shared regulation context and the development of a collaborative learning task to facilitate self-regulation. Based on the learning task framework, we proposed a shared learning interaction consisting of a tutor agent providing external regulation support focusing on the performance of the learner and a peer agent demonstrating co-regulation strategies to promote self-regulation in the learner. A series of user studies have been conducted to understand the learner perceptions about the agent roles, related behaviours and the learning task. Altogether, the work presented in this thesis explores how various roles of agents can be utilised in providing regulation scaffolding to the learners in a socially shared learning context
Schild, Erwan. "De l’importance de valoriser l’expertise humaine dans l’annotation : application à la modélisation de textes en intentions à l’aide d’un clustering interactif." Electronic Thesis or Diss., Université de Lorraine, 2024. http://www.theses.fr/2024LORR0024.
Full textUsually, the task of annotation, used to train conversational assistants, relies on domain experts who understand the subject matter to model. However, data annotation is known to be a challenging task due to its complexity and subjectivity. Therefore, it requires strong analytical skills to model the text in dialogue intention. As a result, most annotation projects choose to train experts in analytical tasks to turn them into "super-experts". In this thesis, we decided instead to focus on the real knowledge of experts by proposing a new annotation method based on Interactive Clustering. This method involves a Human-Machine cooperation, where the machine performs clustering to provide an initial learning base, and the expert annotates MUST-LINK or CANNOT-LINK constraints between the data to iteratively refine the proposed learning base. Such annotation has the advantage of being more instinctive, as experts can associate or differentiate data according to the similarity of their use cases, allowing them to handle the data as they would professionally do on a daily basis. During our studies, we have been able to show that this method significantly reduces the complexity of designing a learning base, notably by reducing the need for training the experts involved in an annotation project. We provide a technical implementation of this method (algorithms and associated graphical interface), as well as a study of optimal parameters to achieve a coherent learning base with minimal annotation. We have also conducted a cost study (both technical and human) to confirm that the use of such a method is realistic in an industrial context. Finally, we provide a set of recommendations to help this method reach its full potential, including: (1) advice aimed at framing the annotation strategy, (2) assistance in identifying and resolving differences of opinion between annotators, (3) rentability indicators for each expert intervention, and (4) methods for analyzing the relevance of the learning base under construction. In conclusion, this thesis provides an innovative approach to design a learning base for a conversational assistant, involving domain experts for their actual knowledge, while requiring a minimum of analytical and technical skills. This work opens the way for more accessible methods for building such assistants
Georgiev, Nikolay. "Assisting physiotherapists by designing a system utilising Interactive Machine Learning." Thesis, Uppsala universitet, Institutionen för informatik och media, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-447489.
Full textKim, Been. "Interactive and interpretable machine learning models for human machine collaboration." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/98680.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (pages 135-143).
I envision a system that enables successful collaborations between humans and machine learning models by harnessing the relative strength to accomplish what neither can do alone. Machine learning techniques and humans have skills that complement each other - machine learning techniques are good at computation on data at the lowest level of granularity, whereas people are better at abstracting knowledge from their experience, and transferring the knowledge across domains. The goal of this thesis is to develop a framework for human-in-the-loop machine learning that enables people to interact effectively with machine learning models to make better decisions, without requiring in-depth knowledge about machine learning techniques. Many of us interact with machine learning systems everyday. Systems that mine data for product recommendations, for example, are ubiquitous. However these systems compute their output without end-user involvement, and there are typically no life or death consequences in the case the machine learning result is not acceptable to the user. In contrast, domains where decisions can have serious consequences (e.g., emergency response panning, medical decision-making), require the incorporation of human experts' domain knowledge. These systems also must be transparent to earn experts' trust and be adopted in their workflow. The challenge addressed in this thesis is that traditional machine learning systems are not designed to extract domain experts' knowledge from natural workflow, or to provide pathways for the human domain expert to directly interact with the algorithm to interject their knowledge or to better understand the system output. For machine learning systems to make a real-world impact in these important domains, these systems must be able to communicate with highly skilled human experts to leverage their judgment and expertise, and share useful information or patterns from the data. In this thesis, I bridge this gap by building human-in-the-loop machine learning models and systems that compute and communicate machine learning results in ways that are compatible with the human decision-making process, and that can readily incorporate human experts' domain knowledge. I start by building a machine learning model that infers human teams' planning decisions from the structured form of natural language of team meetings. I show that the model can infer a human teams' final plan with 86% accuracy on average. I then design an interpretable machine learning model then "makes sense to humans" by exploring and communicating patterns and structure in data to support human decision-making. Through human subject experiments, I show that this interpretable machine learning model offers statistically significant quantitative improvements in interpretability while preserving clustering performance. Finally, I design a machine learning model that supports transparent interaction with humans without requiring that a user has expert knowledge of machine learning technique. I build a human-in-the-loop machine learning system that incorporates human feedback and communicates its internal states to humans, using an intuitive medium for interaction with the machine learning model. I demonstrate the application of this model for an educational domain in which teachers cluster programming assignments to streamline the grading process.
by Been Kim.
Ph. D.
Sahoo, 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.
Full textLagerkvist, Love. "Neural Novelty — How Machine Learning Does Interactive Generative Literature." Thesis, Malmö universitet, Fakulteten för kultur och samhälle (KS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-21222.
Full textGogia, Sumit. "Insight : interactive machine learning for complex graphics selection." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/106021.
Full textThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 89-91).
Modern vector graphics editors support the creation of a wonderful variety of complex designs and artwork. Users produce highly realistic illustrations, stylized representational art, even nuanced data visualizations. In light of these complex graphics, selections, representations of sets of objects that users want to manipulate, become more complex as well. Direct manipulation tools that artists and designers find accessible and useful for editing graphics such as logos and icons do not have the same applicability in these more complex cases. Given that selection is the first step for nearly all editing in graphics, it is important to enable artists and designers to express these complex selections. This thesis explores the use of interactive machine learning techniques to improve direct selection interfaces. To investigate this approach, I created Insight, an interactive machine learning selection tool for making a relevant class of complex selections: visually similar objects. To make a selection, users iteratively provide examples of selection objects by clicking on them in the graphic. Insight infers a selection from the examples at each step, allowing users to quickly understand results of actions and reactively shape the complex selection. The interaction resembles the direct manipulation interactions artists and designers have found accessible, while helping express complex selections by inferring many parameter changes from simple actions. I evaluated Insight in a user study of digital designers and artists, finding that Insight enabled users to effectively and easily make complex selections not supported by state-of-the-art vector graphics editors. My results contribute to existing work by both indicating a useful approach for providing complex representation access to artists and designers, and showing a new application for interactive machine learning.
by Sumit Gogia.
M. Eng.
Bouillon, Manuel. "Apprentissage actif en-ligne d'un classifieur évolutif, application à la reconnaissance de commandes gestuelles." Thesis, Rennes, INSA, 2016. http://www.theses.fr/2016ISAR0019/document.
Full textUsing gesture commands is a new way of interacting with touch sensitive interfaces. In order to facilitate user memorization of several commands, it is essential to let the user customize the gestures. This applicative context gives rise to a crosslearning situation, where the user has to memorize the set of commands and the system has to learn and recognize the different gestures. This situation implies several requirements, from the recognizer and from the system that supervizes its learning process. For instance, the recognizer has to be able to learn from few data samples, to keep learning during its use and to follow indefinitely any change of the data now. The supervisor has to optimize the cooperation between the recognizer and the system to minimize user interactions while maximizing recognizer learning. This thesis presents on the one hand the evolving recognition system Evolve oo, that is capable of fast teaming from few data samples, and that follows concept drifts. On the other hand, this thesis also presents the on line active supervisor lntuiSup, that optimizes user-system cooperation when the user is in the training loop, as during customized gesture command use for instance. The evolving classifier Evolve oo is a fuzzy inference system that is fast learning thanks to the generative capacity of rule premises, and at the same time giving high precision thanks to the discriminative capacity of first order rule conclusion. The use of forgetting in the learning process allows to maintain the learning gain indefinitely, enabling class adding at any stage of system learning, and guaranteeing lifelong evolving capacity. The on line active supervisor IntuiSup optimizes user interactions to train a classifier when the user is in the training loop. The proportion of data that is labeled by the user evolves to adapt to problem difficulty and to follow environment evolution (concept drift s). The use of a boosting method optimizes the timing of user interactions to maximize their impact on classifier learning process
Md, Noor Mohammad Faizuddin. "Machine learning techniques for implicit interaction using mobile sensors." Thesis, University of Glasgow, 2016. http://theses.gla.ac.uk/7723/.
Full textTurner, Jonathan Milton. "Obstacle avoidance and path traversal using interactive machine learning /." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd1905.pdf.
Full textWood, David K. "Learning from Gross Motion Observations of Human-Machine Interaction." Thesis, The University of Sydney, 2011. https://hdl.handle.net/2123/29223.
Full textTurner, Jonathan M. "Obstacle Avoidance and Path Traversal Using Interactive Machine Learning." BYU ScholarsArchive, 2007. https://scholarsarchive.byu.edu/etd/1006.
Full textGustafson, Jonas. "Using Machine Learning to Identify Potential Problem Gamblers." Thesis, Umeå universitet, Institutionen för tillämpad fysik och elektronik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-163640.
Full textChen, Si. "Active Learning Under Limited Interaction with Data Labeler." Thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/104894.
Full textM.S.
Machine Learning (ML) has achieved huge success in recent years. Machine Learning technologies such as recommendation system, speech recognition and image recognition play an important role on human daily life. This success mainly build upon the use of large amount of labeled data: Compared with traditional programming, a ML algorithm does not rely on explicit instructions from human; instead, it takes the data along with the label as input, and aims to learn a function that can correctly map data to the label space by itself. However, data labeling requires human effort and could be time-consuming and expensive especially for datasets that contain domain-specific knowledge (e.g., disease prediction etc.) Active Learning (AL) is one of the solution to reduce data labeling effort. Specifically, the learning algorithm actively selects data points that provide more information for the model, hence a better model can be achieved with less labeled data. While traditional AL strategies do achieve good performance, it requires a small amount of labeled data as initialization and performs data selection in multi-round, which pose great challenge to its application, as there is no platform provide timely online interaction with data labeler and the interaction is often time inefficient. To deal with the limitations, we first propose DULO which a new setting of AL is studied: data selection is only allowed to be performed once. To further broaden the application of our method, we propose D²ULO which is built upon DULO and Domain Adaptation techniques to avoid the use of initial labeled data. Our experiments show that both of the proposed two frameworks achieve better performance compared with state-of-the-art baselines.
Holmberg, Lars. "Human In Command Machine Learning." Licentiate thesis, Malmö universitet, Malmö högskola, Institutionen för datavetenskap och medieteknik (DVMT), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-42576.
Full textAlcoverro, Vidal Marcel. "Stochastic optimization and interactive machine learning for human motion analysis." Doctoral thesis, Universitat Politècnica de Catalunya, 2014. http://hdl.handle.net/10803/285337.
Full textL'anàlisi del moviment humà a partir de dades visuals és un tema central en la recerca en visió per computador, per una banda perquè habilita un ampli espectre d'aplicacions i per altra perquè encara és un problema no resolt quan és aplicat en escenaris no controlats. L'analisi del moviment humà s'utilitza a l'indústria de l'entreteniment per la producció de pel·lícules i videojocs, en aplicacions mèdiques per rehabilitació o per estudis bio-mecànics. També s'utilitza en el camp de la interacció amb computadors o també per l'analisi de grans volums de dades de xarxes socials com Youtube o Flickr, per mencionar alguns exemples. En aquesta tesi s'han estudiat tècniques per l'anàlisi de moviment humà enfocant la seva aplicació en entorns de sales intel·ligents. És a dir, s'ha enfocat a mètodes que puguin permetre l'anàlisi del comportament de les persones a la sala, que permetin la interacció amb els dispositius d'una manera natural i, en general, mètodes que incorporin les computadores en espais on hi ha activitat de persones, per habilitar nous serveis de manera que no interfereixin en la activitat. A la primera part, es proposa un marc genèric per l'ús de filtres de partícules jeràrquics (HPF) especialment adequat per tasques de captura de moviment humà. La captura de moviment humà generalment implica seguiment i optimització de vectors d'estat de molt alta dimensió on a la vegada també s'han de tractar pdf's multi-modals. Els HPF permeten tractar aquest problema mitjançant multiples passades en subdivisions del vector d'estat. Basant-nos en el marc dels HPF, es proposa un mètode per estimar l'antropometria del subjecte, que a la vegada permet obtenir un model acurat del subjecte. També proposem dos nous mètodes per la captura de moviment humà. Per una banda, el APO es basa en una nova estratègia per les funcions de cost basada en la partició de les observacions. Per altra, el DD-HPF utilitza deteccions de parts del cos per millorar la propagació de partícules i l'avaluació de pesos. Ambdós mètodes són integrats dins el marc dels HPF. La segona part de la tesi es centra en la detecció de gestos, i s'ha enfocat en el problema de reduir els esforços d'anotació i entrenament requerits per entrenar un detector per un gest concret. Per tal de reduir els esforços requerits per entrenar un detector de gestos, proposem una solució basada en online random forests que permet l'entrenament en temps real, mentre es reben noves dades sequencialment. El principal aspecte que fa la solució efectiva és el mètode que proposem per obtenir mostres negatives rellevants, mentre s'entrenen els arbres de decisió. El mètode utilitza el detector entrenat fins al moment per recollir mostres basades en la resposta del detector, de manera que siguin més rellevants per l'entrenament. D'aquesta manera l'entrenament és més efectiu pel que fa al nombre de mostres anotades que es requereixen.
Patel, Vedang Vikrambhai. "Reduced Order Modeling For Fluid Structure Interaction Using Machine Learning." The Ohio State University, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1619276050317241.
Full textXia, Guangyu. "Expressive Collaborative Music Performance via Machine Learning." Research Showcase @ CMU, 2016. http://repository.cmu.edu/dissertations/784.
Full textWillman, Martin. "Machine Learning to identify cheaters in online games." Thesis, Umeå universitet, Institutionen för tillämpad fysik och elektronik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-170973.
Full textChen, Li. "Searching for significant feature interaction from biological data." Diss., Online access via UMI:, 2007.
Find full textCharif, Omar. "Modelling and simulating individual's mobility : case study of Luxembourg and its greater region." Thesis, Compiègne, 2013. http://www.theses.fr/2013COMP2130.
Full textIn the last century, transport and in particular the use of private cars has emerged as a major source of CO2 emissions (second behinf energy production). Several cities in the world have put in place strategies to deal with this problem and to reduce its adverse enviromental impacts. Some strategies could not achieve their objectives, and had negative reactions from individuals. The ail of this PhD thesis is to propose a methodology and a platform for modelling and simulating people mobility systems. The developed plat form is, then, used to implement land use and transportation scenarios and strategies in a virtual world to study their impact on human behavior in terms of mobility. To develop this platform, we propose a hybrid model, combining cellular automata and multi-agent systems, capable of handling the complexity of the mobility system able to present it at various spatial ans temporal scales
Stenström, Albin. "Clicking using the eyes, a machine learning approach." Thesis, Linköpings universitet, Interaktiva och kognitiva system, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-121834.
Full textRiviere, Jean-Philippe. "Capturing traces of the dance learning process." Electronic Thesis or Diss., université Paris-Saclay, 2020. http://www.theses.fr/2020UPASG054.
Full textThis thesis focuses on designing interactive tools to understand and support dance learning from videos. Dancers' learning practice represents a rich source of information for researchers interested in designing systems that support motor learning. Indeed, dancers embody a wide range of skills that they reuse during new dance sequences learning. However, these skills are in part the result of embodied implicit knowledge. In this thesis, I argue that we can capture and save traces of dancers' embodied knowledge and use them to design interactive tools that support dance learning. My approach is to study real-life dance learning tasks in individual and collaborative settings. Based on the findings from all the studies, I discuss the challenge of capturing embodied knowledge to support dancers’ learning practice. My thesis highlights that although dancers’ learning processes are diverse, similar strategies emerge to structure their learning process. Finally, I bring and discuss new perspectives to the design of movement-based learning tools
Anderson, Corin R. "A machine learning approach to Web personalization /." Thesis, Connect to this title online; UW restricted, 2002. http://hdl.handle.net/1773/6875.
Full textDias, Pedro Ricardo Gomes. "Recommending media content based on machine learning methods." Master's thesis, Faculdade de Ciências e Tecnologia, 2011. http://hdl.handle.net/10362/6581.
Full textInformation is nowadays made available and consumed faster than ever before. This information technology generation has access to a tremendous deal of data and is left with the heavy burden of choosing what is relevant. With the increasing growth of media sources, the amount of content made available to users has become overwhelming and in need to be managed. Recommender systems emerged with the purpose of providing personalized and meaningful content recommendations based on users’ preferences and usage history. Due to their utility and commercial potential, recommender systems integrate many audiovisual content providers and represent one of their most important and valuable services. The goal of this thesis is to develop a recommender system based on matrix factorization methods, capable of providing meaningful and personalized product recommendations to individual users and groups of users, by taking into account users’ rating patterns and biased tendencies, as well as their fluctuations throughout time.
Harbert, Christopher W. Shang Yi. "An application of machine learning techniques to interactive, constraint-based search." Diss., Columbia, Mo. : University of Missouri-Columbia, 2005. http://hdl.handle.net/10355/4324.
Full textThe entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file viewed on (December 12, 2006) Includes bibliographical references.
Parikh, Neena (Neena S. ). "Interactive tools for fantasy football analytics and predictions using machine learning." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/100687.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (pages 83-84).
The focus of this project is multifaceted: we aim to construct robust predictive models to project the performance of individual football players, and we plan to integrate these projections into a web-based application for in-depth fantasy football analytics. Most existing statistical tools for the NFL are limited to the use of macro-level data; this research looks to explore statistics at a finer granularity. We explore various machine learning techniques to develop predictive models for different player positions including quarterbacks, running backs, wide receivers, tight ends, and kickers. We also develop an interactive interface that will assist fantasy football participants in making informed decisions when managing their fantasy teams. We hope that this research will not only result in a well-received and widely used application, but also help pave the way for a transformation in the field of football analytics.
by Neena Parikh.
M. Eng.
Westphal, Florian. "Efficient Document Image Binarization using Heterogeneous Computing and Interactive Machine Learning." Licentiate thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-16797.
Full textScalable resource-efficient systems for big data analytics
Yu, Guoqiang. "Machine Learning to Interrogate High-throughput Genomic Data: Theory and Applications." Diss., Virginia Tech, 2011. http://hdl.handle.net/10919/28980.
Full textPh. D.
Delaunay, Julien. "Explainability for machine learning models : from data adaptability to user perception." Electronic Thesis or Diss., Université de Rennes (2023-....), 2023. http://www.theses.fr/2023URENS076.
Full textThis thesis explores the generation of local explanations for already deployed machine learning models, aiming to identify optimal conditions for producing meaningful explanations considering both data and user requirements. The primary goal is to develop methods for generating explanations for any model while ensuring that these explanations remain faithful to the underlying model and comprehensible to the users. The thesis is divided into two parts. The first enhances a widely used rule-based explanation method to improve the quality of explanations. It then introduces a novel approach for evaluating the suitability of linear explanations to approximate a model. Additionally, it conducts a comparative experiment between two families of counterfactual explanation methods to analyze the advantages of one over the other. The second part focuses on user experiments to assess the impact of three explanation methods and two distinct representations. These experiments measure how users perceive their interaction with the model in terms of understanding and trust, depending on the explanations and representations. This research contributes to a better explanation generation, with potential implications for enhancing the transparency, trustworthiness, and usability of deployed AI systems
Sullivan, Patrick Ryan. "ALJI: Active Listening Journal Interaction." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/95207.
Full textMaster of Science
An incredibly large number of people suffer from depression, and they can rightfully feel trapped or imprisoned by this illness. A very simple way to understand depression is to first imagine looking at the most beautiful sunset you've ever seen, and then imagine feeling absolutely nothing while looking that same sunset, and you can't explain why. When a person is depressed, they are likely to feel like a burden to those around them. This causes them to avoid social gathering and friends, making them isolated away from people that could support them. This worsens their depression and a terrible cycle begins. One of the best ways out of this cycle is to reveal the depression to a doctor or psychologist, and to ask them for guidance. However, many people don't see or realize this excellent option is open to them, and will continue to suffer with depression for far longer than needed. This thesis describes an idea called the Active Listening Journal Interaction, or ALJI. ALJI acts just like someone's personal journal or diary, but it also has some protections from illnesses like depression. First, ALJI searches a journal entry for indicators about the author's health, then ALJI asks the author a few questions to better understand the author, and finally ALJI gives that author information and guidance on improving their health. We are starting to create a computer program of ALJI by first building and testing the detector for the author's health. Instead of making the detector directly, we show the computer some examples of the health indicators from journals we know very well, and then let the computer focus on finding the pattern that would reveal those health indicators from any journal. This is called machine learning, and in our case, ALJI's machine learning is going to be difficult because we have very few example journals where we know all of the health indicators. However, we believe that fixing this issue would solve the first step of ALJI. The end of this thesis also discusses the next steps going forward with ALJI.
Vitt, Artur. "Machine Learning in DigitalTelerehabilitation : Telerehabilitation system based on kinect." Thesis, Linnéuniversitetet, Institutionen för datavetenskap (DV), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-70048.
Full textAgarwal, Ankur. "Machine Learning for Image Based Motion Capture." Phd thesis, Grenoble INPG, 2006. http://tel.archives-ouvertes.fr/tel-00390301.
Full textLetard, Vincent. "Apprentissage incrémental de modèles de domaines par interaction dialogique." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLS100/document.
Full textArtificial Intelligence is the field of research aiming at mimicking or replacing human cognitive abilities. As such, one of its subfields is focused on the progressive automation of the programming process. In other words, the goal is to transfer cognitive load from the human to the system, whether it be autonomous or guided by the user. In this thesis, we investigate the conditions for making a user-guided system autonomous using another subfield of Artificial Intelligence : Machine Learning. As an implementation framework, we chose the design of an incremental operational assistant, that is a system able to react to natural language requests from the user with relevant actions. The system must also be able to learn the correct reactions, incrementally. In our work, the requests are in written French, and the associated actions are represented by corresponding instructions in a programming language (here R and bash). The learning is performed using a set of examples composed by the users themselves while interacting. Thus they progressively define the most relevant actions for each request, making the system more autonomous. We collected several example sets for evaluation of the learning methods, analyzing and reducing the inherent collection biases. The proposed protocol is based on incremental bootstrapping of the system, starting from an empty or limited knowledge base. As a result of this choice, the obtained knowledge base reflects the user needs, the downside being that the overall number of examples is limited. To avoid this problem, after assessing a baseline method, we apply a case base reasoning approach to the request to command transfer problem: formal analogical reasoning. We show that this method yields answers with a very high precision, but also a relatively low coverage. We explore the analogical extension of the example base in order to increase the coverage of the provided answers. We also assess the relaxation of analogical constraints for an increased tolerance of analogical reasoning to noise in the examples. The running delay of the simple analogical approach is already around 1 second, and is badly influenced by both the automatic extension of the base and the relaxation of the constraints. We explored several segmentation strategies on the input examples in order to reduce reduce this time. The delay however remains the main obstacle to using analogical reasoning for natural language processing with usual volumes of data. Finally, the incremental operational assistant based on analogical reasoning was tested in simulated incremental condition in order to assess the learning behavior over time. The system reaches a stable correct answer rate after a dozen examples given in average for each command type. Although the effective performance depends on the total number of accounted commands, this observation opens interesting applicative tracks for the considered task of transferring from a rich source domain (natural language) to a less rich target domain (programming language)
Stoffel, Florian [Verfasser]. "Transparency in Interactive Feature-based Machine Learning : Challenges and Solutions / Florian Stoffel." Konstanz : Bibliothek der Universität Konstanz, 2018. http://d-nb.info/1173616314/34.
Full textDondelinger, Frank. "Machine learning approach to reconstructing signalling pathways and interaction networks in biology." Thesis, University of Edinburgh, 2013. http://hdl.handle.net/1842/7850.
Full textCai, Bingjing. "Machine learning approaches for extracting protein complexes from protein-protein interaction networks." Thesis, Ulster University, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.603536.
Full textSarigul, Erol. "Interactive Machine Learning for Refinement and Analysis of Segmented CT/MRI Images." Diss., Virginia Tech, 2004. http://hdl.handle.net/10919/25954.
Full textPh. D.
Iqbal, Sumaiya. "Machine Learning based Protein Sequence to (un)Structure Mapping and Interaction Prediction." ScholarWorks@UNO, 2017. http://scholarworks.uno.edu/td/2379.
Full textWang, Jianxiong. "A machine learning system for understanding appraisal in design documents." Thesis, The University of Sydney, 2009. https://hdl.handle.net/2123/28237.
Full textLynch, Paul Kieran. "The generation of knowledge based systems for interactive nonlinear constrained optimisation." Thesis, Queen's University Belfast, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.388221.
Full textSanchez, Téo. "Interactive Machine Teaching with and for Novices." Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPASG055.
Full textMachine Learning algorithms in society or interactive technology generally provide users with little or no agency with respect to how models are optimized from data. Only experts design, analyze, and optimize ML algorithms. At the intersection of HCI and ML, the field of Interactive Machine Learning (IML) aims at incorporating ML workflows within existing users' practices. Interactive Machine Teaching (IMT), in particular, focuses on involving non-expert users as "machine teachers" and empowering them in the process of building ML models. Non-experts could take advantage of building ML models to process and automate tasks on their data, leading to more robust and less biased models for specialized problems. This thesis takes an empirical approach to IMT by focusing on how people develop strategies and understand interactive ML systems through the act of teaching. This research provides two user studies involving participants as teachers of image-based classifiers using transfer-learned artificial neural networks. These studies focus on what users understand from the ML model's behavior and what strategy they may use to "make it work." The second study focuses on machine teachers' understanding and use of two types of uncertainty: aleatoric uncertainty, which conveys ambiguity, and epistemic uncertainty, which conveys novelty. I discuss the use of uncertainty and active learning in IMT. Finally, I report artistic collaborations and adopt an auto-ethnographic approach to challenges and opportunities for developing IMT with artists. I argue that people develop different teaching strategies that can evolve with insights obtained throughout the interaction. People's teaching strategies structure the composition of the data they curated and affect their ability to understand and predict the algorithm behavior. Besides empowering people to build ML models, IMT can foster investigative behaviors, leveraging peoples' literacy in ML and artificial intelligence
Billewicz, Agnieszka. "Study of a relationship. Designerly explorations of machine learning algorithms." Thesis, Malmö högskola, Fakulteten för kultur och samhälle (KS), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-22593.
Full textAkrour, Riad. "Robust Preference Learning-based Reinforcement Learning." Thesis, Paris 11, 2014. http://www.theses.fr/2014PA112236/document.
Full textThe thesis contributions resolves around sequential decision taking and more precisely Reinforcement Learning (RL). Taking its root in Machine Learning in the same way as supervised and unsupervised learning, RL quickly grow in popularity within the last two decades due to a handful of achievements on both the theoretical and applicative front. RL supposes that the learning agent and its environment follow a stochastic Markovian decision process over a state and action space. The process is said of decision as the agent is asked to choose at each time step an action to take. It is said stochastic as the effect of selecting a given action in a given state does not systematically yield the same state but rather defines a distribution over the state space. It is said to be Markovian as this distribution only depends on the current state-action pair. Consequently to the choice of an action, the agent receives a reward. The RL goal is then to solve the underlying optimization problem of finding the behaviour that maximizes the sum of rewards all along the interaction of the agent with its environment. From an applicative point of view, a large spectrum of problems can be cast onto an RL one, from Backgammon (TD-Gammon, was one of Machine Learning first success giving rise to a world class player of advanced level) to decision problems in the industrial and medical world. However, the optimization problem solved by RL depends on the prevous definition of a reward function that requires a certain level of domain expertise and also knowledge of the internal quirks of RL algorithms. As such, the first contribution of the thesis was to propose a learning framework that lightens the requirements made to the user. The latter does not need anymore to know the exact solution of the problem but to only be able to choose between two behaviours exhibited by the agent, the one that matches more closely the solution. Learning is interactive between the agent and the user and resolves around the three main following points: i) The agent demonstrates a behaviour ii) The user compares it w.r.t. to the current best one iii) The agent uses this feedback to update its preference model of the user and uses it to find the next behaviour to demonstrate. To reduce the number of required interactions before finding the optimal behaviour, the second contribution of the thesis was to define a theoretically sound criterion making the trade-off between the sometimes contradicting desires of complying with the user's preferences and demonstrating sufficiently different behaviours. The last contribution was to ensure the robustness of the algorithm w.r.t. the feedback errors that the user might make. Which happens more often than not in practice, especially at the initial phase of the interaction, when all the behaviours are far from the expected solution