Rozprawy doktorskie na temat „Machine learnings”
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Algohary, Ahmad. "PROSTATE CANCER RISK STRATIFICATION USING RADIOMICS FOR PATIENTS ON ACTIVE SURVEILLANCE: MULTI-INSTITUTIONAL USE CASES". Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1599231033923829.
Pełny tekst źródłaStohr, Daniel Christoph [Verfasser]. "Die beruflichen Anforderungen der Digitalisierung hinsichtlich formaler, physischer und kompetenzspezifischer Aspekte : Eine Analyse von Stellenanzeigen mittels Methoden des Text Minings und Machine Learnings / Daniel Christoph Stohr". Frankfurt a.M. : Peter Lang GmbH, Internationaler Verlag der Wissenschaften, 2019. http://d-nb.info/1185347240/34.
Pełny tekst źródłaTebbifakhr, Amirhossein. "Machine Translation For Machines". Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/320504.
Pełny tekst źródłaDinakar, Karthik. "Lensing Machines : representing perspective in machine learning". Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/112523.
Pełny tekst źródłaCataloged from PDF version of thesis. Due to the condition of the original material with text runs off the edges of the pages, the reproduction may have unavoidable flaws.
Includes bibliographical references (pages 167-172).
Generative models are venerated as full probabilistic models that randomly generate observable data given a set of latent variables that cannot be directly observed. They can be used to simulate values for variables in the model, allowing analysis by synthesis or model criticism, towards an iterative cycle of model specification, estimation, and critique. However, many datasets represent a combination of several viewpoints - different ways of looking at the same data that leads to various generalizations. For example, a corpus that has data generated by multiple people may be mixtures of several perspectives and can be viewed with different opinions by others. It isn't always possible to represent the viewpoints by clean separation, in advance, of examples representing each perspective and train a separate model for each point of view. In this thesis, we introduce lensing, a mixed-initiative technique to (i) extract lenses or mappings between machine-learned representations and perspectives of human experts, and (2) generate lensed models that afford multiple perspectives of the same dataset. We explore lensing of latent variable model in their configuration, parameter and evidential spaces. We apply lensing to three health applications, namely imbuing the perspectives of experts into latent variable models that analyze adolescent distress and crisis counseling.
by Karthik Dinakar.
Ph. D.
Roderus, Jens, Simon Larson i Eric Pihl. "Hadoop scalability evaluation for machine learning algorithms on physical machines : Parallel machine learning on computing clusters". Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-20102.
Pełny tekst źródłaKent, W. F. "Machine learning for parameter identification of electric induction machines". Thesis, University of Liverpool, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.399178.
Pełny tekst źródłaThorén, Daniel. "Radar based tank level measurement using machine learning : Agricultural machines". Thesis, Linköpings universitet, Programvara och system, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176259.
Pełny tekst źródłaRomano, Donato. "Machine Learning algorithms for predictive diagnostics applied to automatic machines". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/22319/.
Pełny tekst źródłaSchneider, C. "Using unsupervised machine learning for fault identification in virtual machines". Thesis, University of St Andrews, 2015. http://hdl.handle.net/10023/7327.
Pełny tekst źródłaSOAVE, Elia. "Diagnostics and prognostics of rotating machines through cyclostationary methods and machine learning". Doctoral thesis, Università degli studi di Ferrara, 2022. http://hdl.handle.net/11392/2490999.
Pełny tekst źródłaNegli ultimi decenni, l’analisi vibrazionale è stata sfruttata per il monitoraggio di molti sistemi meccanici per applicazioni industriali. Nonostante molte pubblicazioni abbiano dimostrato come la diagnostica vibrazionale possa raggiungere risultati soddisfacenti, lo scenario industriale odierno è in profondo cambiamento, guidato dalla necessità di ridurre tempi e costi produttivi. In questa direzione, la ricerca deve concentrarsi sul miglioramento dell’efficienza computazionale delle tecniche di analisi del segnale applicate a fini diagnostici. Allo stesso modo, il mondo industriale richiede una sempre maggior attenzione per la manutenzione predittiva, al fine di stimare l’effettivo danneggiamento del sistema evitando così inutili fermi macchina per operazioni manutentive. In tale ambito, negli ultimi anni l’attività di ricerca si sta spostando verso lo sviluppo di modelli prognostici finalizzati alla stima della vita utile residua dei componenti. Tuttavia, è importante ricordare come i due ambiti siano strettamente connessi, essendo la diagnostica la base su cui fondare l’efficacia di ciascun modello prognostico. Su questa base, questa tesi è stata incentrata su queste due diverse, ma tra loro connesse, aree al fine di identificare e predire possibile cause di cedimento su macchine rotanti per applicazioni industriali. La prima parte della tesi è concentrata sullo sviluppo di un nuovo indicatore di blind deconvolution per l’identificazione di difetti su organi rotanti sulla base della teoria ciclostazionaria. Il criterio presentato vuole andare a ridurre il costo computazionale richiesto dalla blind deconvolution tramite l’utilizzo della serie di Fourier-Bessel grazie alla sua natura modulata, maggiormente affine alla tipica firma vibratoria del difetto. L’indicatore proposto viene accuratamente confrontato con il suo analogo basato sulla classica serie di Fourier considerando sia segnali simulati che segnali di vibrazione reali. Il confronto vuole dimostrare il miglioramento fornito dal nuovo criterio in termini sia di minor numero di operazioni richieste dall’algoritmo che di efficacia diagnostica anche in condizioni di segnale molto rumoroso. Il contributo innovativo di questa parte riguarda la combinazione di ciclostazionarietà e serie di Furier-Bessel che porta alla definizione di un nuovo criterio di blind deconvolution in grado di mantenere l’efficacia diagnostica della ciclostazionarietà ma con un minor tempo computazionale per venire incontro alle richieste del mondo industriale. La second parte riguarda la definizione di un nuovo modello prognostico, appartenente alla famiglia degli hidden Markov models, costruito partendo da una distribuzione Gaussiana generalizzata. L’obbiettivo del metodo proposto è una miglior riproduzione della reale distribuzione dei dati, in particolar modo negli ultimi stadi del danneggiamento. Infatti, la comparsa e l’evoluzione del difetto comporta una modifica della distribuzione delle osservazioni fra i diversi stati. Di conseguenza, una densità di probabilità generalizzata permette la modificazione della forma della distribuzione tramite diversi valori dei parametri del modello. Il metodo proposto viene confrontato con il classico hidden Markov model di base Gaussiana in termini di qualità di riproduzione della distribuzione e predizione della sequenza di stati tramite l’analisi di alcuni test di rottura su cuscinetti volventi e sistemi complessi. L’innovatività di questa parte è data dalla definizione di un algoritmo iterativo per la stima dei parametri del modello nell’ipotesi di distribuzione Gaussiana generalizzata, sia nel caso monovariato che multivariato, partendo dalle osservazioni sul sistema fisico in esame.
Andersson, Viktor. "Machine Learning in Logistics: Machine Learning Algorithms : Data Preprocessing and Machine Learning Algorithms". Thesis, Luleå tekniska universitet, Datavetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-64721.
Pełny tekst źródłaData Ductus är ett svenskt IT-konsultbolag, deras kundbas sträcker sig från små startups till stora redan etablerade företag. Företaget har stadigt växt sedan 80-talet och har etablerat kontor både i Sverige och i USA. Med hjälp av maskininlärning kommer detta projket att presentera en möjlig lösning på de fel som kan uppstå inom logistikverksamheten, orsakade av den mänskliga faktorn.Ett sätt att förbehandla data innan den tillämpas på en maskininlärning algoritm, liksom ett par algoritmer för användning kommer att presenteras.
Angola, Enrique. "Novelty Detection Of Machinery Using A Non-Parametric Machine Learning Approach". ScholarWorks @ UVM, 2018. https://scholarworks.uvm.edu/graddis/923.
Pełny tekst źródłaGustafsson, Robin, i Lucas Fröjdendahl. "Machine Learning for Traffic Control of Unmanned Mining Machines : Using the Q-learning and SARSA algorithms". Thesis, KTH, Hälsoinformatik och logistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-260285.
Pełny tekst źródłaManuell konfigurering av trafikkontroll för obemannade gruvmaskiner kan vara en tidskrävande process. Om denna konfigurering skulle kunna automatiseras så skulle det gynnas tidsmässigt och ekonomiskt. Denna rapport presenterar en lösning med maskininlärning med Q-learning och SARSA som tillvägagångssätt. Resultaten visar på att konfigureringstiden möjligtvis kan tas ned från 1–2 veckor till i värsta fallet 6 timmar vilket skulle minska kostnaden för produktionssättning. Tester visade att den slutgiltiga lösningen kunde köra kontinuerligt i 24 timmar med minst 82% träffsäkerhet jämfört med 100% då den manuella konfigurationen används. Slutsatsen är att maskininlärning eventuellt kan användas för automatisk konfiguration av trafikkontroll. Vidare arbete krävs för att höja träffsäkerheten till 100% så att det kan användas istället för manuell konfiguration. Fler studier bör göras för att se om detta även är sant och applicerbart för mer komplexa scenarier med större gruvlayouts och fler maskiner.
Tarsa, Stephen J. "Machine Learning for Machines: Data-Driven Performance Tuning at Runtime Using Sparse Coding". Thesis, Harvard University, 2015. http://nrs.harvard.edu/urn-3:HUL.InstRepos:14226074.
Pełny tekst źródłaBorngrund, Carl. "Machine vision for automation of earth-moving machines : Transfer learning experiments with YOLOv3". Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-75169.
Pełny tekst źródłaMINERVINI, MARCELLO. "Multi-sensor analysis and machine learning classification approach for diagnostics of electrical machines". Doctoral thesis, Università degli studi di Pavia, 2022. http://hdl.handle.net/11571/1464785.
Pełny tekst źródłaLiu, Yi. "Studies on support vector machines and applications to video object extraction". Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1158588434.
Pełny tekst źródłaStiernborg, Sebastian, i Sara Ervik. "Evaluation of Machine Learning Classification Methods : Support Vector Machines, Nearest Neighbour and Decision Tree". Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-209119.
Pełny tekst źródłaMed växande data och tillgänglighet ökar intresset och användning- en för maskininlärning, tillsammans med behovet för klassificering. Klassificering är en viktig metod inom maskininlärning för att förenk- la data och göra förutstägelser. Denna rapport utvärderar tre klassificeringsmetoder för övervakad in- lärning: Stödvektormaskiner (SVM) med olika kärnor, Närmaste Gran- ne (k-NN) och Beslutsträd (DT). Metoderna utvärderades baserat på nogrannhet, precision, återkallelse och tid. Experimenten utfördes på artificiell data skapad för att representera en variation av fördelningar med en begränsning av endast 2 egenskaper och 3 klasser. Resultaten visar att mätningarna för noggrannhet och tid varierar avsevärt för olika variationer av dataset. SVM med RBF-kärna gav generellt högre värden för noggrannhet i jämförelse med de and- ra klassificeringsmetoderna. k-NN visade något lägre noggrannhet än SVM med RBF-kärna i allmänhet, men presterade bättre på det mest utmanande datasetet. DT är den minst tidskrävande algoritmen och var signifikant snabbare än de andra klassificeringsmetoderna. Den enda metoden som kunde konkurrera med DT i tid var SVM med k- NN som var snabbare än DT för det dataset som hade liten spridning och sammanfallande klasser. Även om en tydlig trend kan ses i resultaten behöver området studeras ytterligare för att dra en omfattande slutsats på grund av begränsning av dataset i denna studie.
Collazo, Santiago Bryan Omar. "Machine learning blocks". Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/100301.
Pełny tekst źródłaThis 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.
This work presents MLBlocks, a machine learning system that lets data scientists explore the space of modeling techniques in a very easy and efficient manner. We show how the system is very general in the sense that virtually any problem and dataset can be casted to use MLBlocks, and how it supports the exploration of Discriminative Modeling, Generative Modeling and the use of synthetic features to boost performance. MLBlocks is highly parameterizable, and some of its powerful features include the ease of formulating lead and lag experiments for time series data, its simple interface for automation, and its extensibility to additional modeling techniques. We show how we used MLBlocks to quickly get results for two very different realworld data science problems. In the first, we used time series data from Massive Open Online Courses to cast many lead and lag formulations of predicting student dropout. In the second, we used MLBlocks' Discriminative Modeling functionality to find the best-performing model for predicting the destination of a car given its past trajectories. This later functionality is self-optimizing and will find the best model by exploring a space of 11 classification algorithms with a combination of Multi-Armed Bandit strategies and Gaussian Process optimizations, all in a distributed fashion in the cloud.
by Bryan Omar Collazo Santiago.
M. Eng.
Cardamone, Dario. "Support Vector Machine a Machine Learning Algorithm". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017.
Znajdź pełny tekst źródłaShukla, Ritesh. "Machine learning ecosystem : implications for business strategy centered on machine learning". Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/107342.
Pełny tekst źródłaCataloged from PDF version of thesis.
Includes bibliographical references (pages 48-50).
As interest for adopting machine learning as a core component of a business strategy increases, business owners face the challenge of integrating an uncertain and rapidly evolving technology into their organization, and depending on this for the success of their strategy. The field of Machine learning has a rich set of literature for modeling of technical systems that implement machine learning. This thesis attempts to connect the literature for business and technology and for evolution and adoption of technology to the emergent properties of machine learning systems. This thesis provides high-level levers and frameworks to better prepare business owners to adopt machine learning to satisfy their strategic goals.
by Ritesh Shukla.
S.M. in Engineering and Management
Huembeli, Patrick. "Machine learning for quantum physics and quantum physics for machine learning". Doctoral thesis, Universitat Politècnica de Catalunya, 2021. http://hdl.handle.net/10803/672085.
Pełny tekst źródłaLa investigación en la intersección del aprendizaje automático (machine learning, ML) y la física cuántica es una área en crecimiento reciente debido al éxito y las enormes expectativas de ambas áreas. ML es posiblemente una de las tecnologías más prometedoras que ha alterado y seguirá alterando muchos aspectos de nuestras vidas. Es casi seguro que la forma en que investigamos no es una excepción y el ML, con su capacidad sin precedentes para encontrar patrones ocultos en los datos ayudará a futuros descubrimientos científicos. La física cuántica, por otro lado, aunque a veces no es del todo intuitiva, es una de las teorías físicas más exitosas, y además estamos a punto de adoptar algunas tecnologías cuánticas en nuestra vida diaria. La física cuántica de los muchos cuerpos (many-body) es una subárea de la física cuántica donde estudiamos el comportamiento colectivo de partículas o átomos y la aparición de fenómenos que se deben a este comportamiento colectivo, como las fases de la materia. El estudio de las transiciones de fase de estos sistemas a menudo requiere cierta intuición de cómo podemos cuantificar el parámetro de orden de una fase. Los algoritmos de ML pueden imitar algo similar a la intuición al inferir conocimientos a partir de datos de ejemplo. Por lo tanto, pueden descubrir patrones que son invisibles para el ojo humano, lo que los convierte en excelentes candidatos para estudiar las transiciones de fase. Al mismo tiempo, se sabe que los dispositivos cuánticos pueden realizar algunas tareas computacionales exponencialmente más rápido que los ordenadores clásicos y pueden producir patrones de datos que son difíciles de simular en los ordenadores clásicos. Por lo tanto, existe la esperanza de que los algoritmos ML que se ejecutan en dispositivos cuánticos muestren una ventaja sobre su analógico clásico. Estudiamos dos caminos diferentes a lo largo de la vanguardia del ML y la física cuántica. Por un lado, estudiamos el uso de redes neuronales (neural network, NN) para clasificar las fases de la materia en sistemas cuánticos de muchos cuerpos. Por otro lado, estudiamos los algoritmos ML que se ejecutan en ordenadores cuánticos. La conexión entre ML para la física cuántica y la física cuántica para ML en esta tesis es un subárea emergente en ML: la interpretabilidad de los algoritmos de aprendizaje. Un ingrediente crucial en el estudio de las transiciones de fase con NN es una mejor comprensión de las predicciones de la NN, para inferir un modelo del sistema cuántico. Así pues, la interpretabilidad de la NN puede ayudarnos en este esfuerzo. El estudio de la interpretabilitad inspiró además un estudio en profundidad de la pérdida de aplicaciones de aprendizaje automático cuántico (quantum machine learning, QML) que también discutiremos. En esta tesis damos respuesta a las preguntas de cómo podemos aprovechar las NN para clasificar las fases de la materia y utilizamos un método que permite hacer una adaptación de dominio para transferir la "intuición" aprendida de sistemas sin ruido a sistemas con ruido. Para mapear el diagrama de fase de los sistemas cuánticos de muchos cuerpos de una manera totalmente no supervisada, estudiamos un método conocido de detección de anomalías que nos permite reducir la entrada humana al mínimo. También usaremos métodos de interpretabilidad para estudiar las NN que están entrenadas para distinguir fases de la materia para comprender si las NN están aprendiendo algo similar a un parámetro de orden y si su forma de aprendizaje puede ser más accesible para los humanos. Y finalmente, inspirados por la interpretabilidad de las NN clásicas, desarrollamos herramientas para estudiar los paisajes de pérdida de los circuitos cuánticos variacionales para identificar posibles diferencias entre los algoritmos ML clásicos y cuánticos que podrían aprovecharse para obtener una ventaja cuántica.
Menke, Joshua E. "Improving machine learning through oracle learning /". Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd1726.pdf.
Pełny tekst źródłaMenke, Joshua Ephraim. "Improving Machine Learning Through Oracle Learning". BYU ScholarsArchive, 2007. https://scholarsarchive.byu.edu/etd/843.
Pełny tekst źródłaPapakyriakopoulos, Orestis [Verfasser], Simon [Akademischer Betreuer] Hegelich, Jürgen [Gutachter] Pfeffer i Simon [Gutachter] Hegelich. "Political Machines: Machine learning for understanding the politics of social machines / Orestis Papakyriakopoulos ; Gutachter: Jürgen Pfeffer, Simon Hegelich ; Betreuer: Simon Hegelich". München : Universitätsbibliothek der TU München, 2020. http://d-nb.info/121302627X/34.
Pełny tekst źródłaCraddock, Richard Cameron. "Support vector classification analysis of resting state functional connectivity fMRI". Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31774.
Pełny tekst źródłaCommittee Chair: Hu, Xiaoping; Committee Co-Chair: Vachtsevanos, George; Committee Member: Butera, Robert; Committee Member: Gurbaxani, Brian; Committee Member: Mayberg, Helen; Committee Member: Yezzi, Anthony. Part of the SMARTech Electronic Thesis and Dissertation Collection.
Mauricio, Palacio Sebastián. "Machine-Learning Applied Methods". Doctoral thesis, Universitat de Barcelona, 2020. http://hdl.handle.net/10803/669286.
Pełny tekst źródłaPace, Aaron J. "Guided Interactive Machine Learning". Diss., CLICK HERE for online access, 2006. http://contentdm.lib.byu.edu/ETD/image/etd1355.pdf.
Pełny tekst źródłaMontanez, George D. "Why Machine Learning Works". Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/1114.
Pełny tekst źródłaThomaz, Andrea Lockerd. "Socially guided machine learning". Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/36160.
Pełny tekst źródłaIncludes bibliographical references (p. 139-146).
Social interaction will be key to enabling robots and machines in general to learn new tasks from ordinary people (not experts in robotics or machine learning). Everyday people who need to teach their machines new things will find it natural for to rely on their interpersonal interaction skills. This thesis provides several contributions towards the understanding of this Socially Guided Machine Learning scenario. While the topic of human input to machine learning algorithms has been explored to some extent, prior works have not gone far enough to understand what people will try to communicate when teaching a machine and how algorithms and learning systems can be modified to better accommodate a human partner. Interface techniques have been based on intuition and assumptions rather than grounded in human behavior, and often techniques are not demonstrated or evaluated with everyday people. Using a computer game, Sophie's Kitchen, an experiment with human subjects provides several insights about how people approach the task of teaching a machine. In particular, people want to direct and guide an agent's exploration process, they quickly use the behavior of the agent to infer a mental model of the learning process, and they utilize positive and negative feedback in asymmetric ways.
(cont.) Using a robotic platform, Leonardo, and 200 people in follow-up studies of modified versions of the Sophie's Kitchen game, four research themes are developed. The use of human guidance in a machine learning exploration can be successfully incorporated to improve learning performance. Novel learning approaches demonstrate aspects of goal-oriented learning. The transparency of the machine learner can have significant effects on the nature of the instruction received from the human teacher, which in turn positively impacts the learning process. Utilizing asymmetric interpretations of positive and negative feedback from a human partner, can result in a more efficient and robust learning experience.
by Andrea Lockerd Thomaz.
Ph.D.
Leather, Hugh. "Machine learning in compilers". Thesis, University of Edinburgh, 2011. http://hdl.handle.net/1842/9810.
Pełny tekst źródłaArmani, Luca. "Machine Learning: Customer Segmentation". Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24925/.
Pełny tekst źródłaDu, Buisson Lise. "Machine learning in astronomy". Master's thesis, University of Cape Town, 2015. http://hdl.handle.net/11427/15502.
Pełny tekst źródłaPunugu, Venkatapavani Pallavi. "Machine Learning in Neuroimaging". Thesis, State University of New York at Buffalo, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10284048.
Pełny tekst źródłaThe application of machine learning algorithms to analyze and determine disease related patterns in neuroimaging has emerged to be of extreme interest in Computer-Aided Diagnosis (CAD). This study is a small step towards categorizing Alzheimer's disease, Neurode-generative diseases, Psychiatric diseases and Cerebrovascular Small Vessel diseases using CAD. In this study, the SPECT neuroimages are pre-processed using powerful data reduction techniques such as Singular Value Decomposition (SVD), Independent Component Analysis (ICA) and Automated Anatomical Labeling (AAL). Each of the pre-processing methods is used in three machine learning algorithms namely: Artificial Neural Networks (ANNs), Support Vector Machines (SVMs) and k-Nearest Neighbors (k-nn) to recognize disease patterns and classify the diseases. While neurodegenerative diseases and psychiatric diseases overlap with a mix of diseases and resulted in fairly moderate classification, the classification between Alzheimer's disease and Cerebrovascular Small Vessel diseases yielded good results with an accuracy of up to 73.7%.
Lounici, Sofiane. "Watermarking machine learning models". Electronic Thesis or Diss., Sorbonne université, 2022. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2022SORUS282.pdf.
Pełny tekst źródłaThe protection of the intellectual property of machine learning models appears to be increasingly necessary, given the investments and their impact on society. In this thesis, we propose to study the watermarking of machine learning models. We provide a state of the art on current watermarking techniques, and then complement it by considering watermarking beyond image classification tasks. We then define forging attacks against watermarking for model hosting platforms and present a new fairness-based watermarking technique. In addition, we propose an implementation of the presented techniques
Tiensuu, Jacob, Maja Linderholm, Sofia Dreborg i Fredrik Örn. "Detecting exoplanets with machine learning : A comparative study between convolutional neural networks and support vector machines". Thesis, Uppsala universitet, Institutionen för teknikvetenskaper, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-385690.
Pełny tekst źródłaDelnevo, Giovanni <1991>. "On the implications of big data and machine learning in the interplay between humans and machines". Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amsdottorato.unibo.it/10036/1/phd_thesis_delnevo_giovanni.pdf.
Pełny tekst źródłaMwamsojo, Nickson. "Neuromorphic photonic systems for information processing". Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAS002.
Pełny tekst źródłaArtificial Intelligence has revolutionized the scientific community thanks to the advent of a robust computation workforce and Artificial Neural Neural Networks. However, the current implementation trends introduce a rapidly growing demand for computational power surpassing the rates and limitations of Moore's and Koomey's Laws, which implies an eventual efficiency barricade. To respond to these demands, bio-inspired techniques, known as 'neuro-morphic' systems, are proposed using physical devices. Of these systems, we focus on 'Reservoir Computing' and 'Coherent Ising Machines' in our works.Reservoir Computing, for instance, demonstrated its computation power such as the state-of-the-art performance of up to 1 million words per second using photonic hardware in 2017. We propose an automatic hyperparameter tuning algorithm for Reservoir Computing and give a theoretical study of its convergence. Moreover, we propose Reservoir Computing for early-stage Alzheimer's disease detection with a thorough assessment of the energy costs versus performance compromise. Finally, we confront the noisy image restoration problem by maximum a posteriori using an optoelectronic implementation of a Coherent Ising Machine
Johansson, Richard. "Machine learning på tidsseriedataset : En utvärdering av modeller i Azure Machine Learning Studio". Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-71223.
Pełny tekst źródłaWu, Anjian M. B. A. Sloan School of Management. "Performance modeling of human-machine interfaces using machine learning". Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/122599.
Pełny tekst źródłaThesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019, In conjunction with the Leaders for Global Operations Program at MIT
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 70-71).
As the popularity of online retail expands, world-class electronic commerce (e-commerce) businesses are increasingly adopting collaborative robotics and Internet of Things (IoT) technologies to enhance fulfillment efficiency and operational advantage. E-commerce giants like Alibaba and Amazon are known to have smart warehouses staffed by both machines and human operators. The robotics systems specialize in transporting and maneuvering heavy shelves of goods to and from operators. Operators are left to higher-level cognitive tasks needed to process goods such as identification and complex manipulation of individual objects. Achieving high system throughput in these systems require harmonized interaction between humans and machines. The robotics systems must minimize time that operators are waiting for new work (idle time) and operators need to minimize time processing items (takt time). Over time, these systems will naturally generate extensive amounts of data. Our research provides insights into both using this data to design a machine-learning (ML) model of takt time, as well as exploring methods of interpreting insights from such a model. We start by presenting our iterative approach to developing a ML model that predicts the average takt of a group of operators at hourly intervals. Our final XGBoost model reached an out-of-sample performance of 4.01% mean absolute percent error (MAPE) using over 250,000 hours of historic data across multiple warehouses around the world. Our research will share methods to cross-examine and interpret the relationships learned by the model for business value. This can allow organizations to effectively quantify system trade-offs as well as identify root-causes of takt performance deviations. Finally, we will discuss the implications of our empirical findings.
by Anjian Wu.
M.B.A.
S.M.
M.B.A. Massachusetts Institute of Technology, Sloan School of Management
S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
Pilkington, Nicholas Charles Victor. "Hyperparameter optimisation for multiple kernels". Thesis, University of Cambridge, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.648763.
Pełny tekst źródłaMerat, Sepehr. "Clustering Via Supervised Support Vector Machines". ScholarWorks@UNO, 2008. http://scholarworks.uno.edu/td/857.
Pełny tekst źródłaOhlsson, Caroline. "Exploring the potential of machine learning : How machine learning can support financial risk management". Thesis, Uppsala universitet, Företagsekonomiska institutionen, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-324684.
Pełny tekst źródłaGhule, S. "Computational development of the strategies to explore molecular machines and the molecular space for desired properties using machine learning". Thesis(Ph.D.), CSIR-National Chemical Laboratory, Pune, 2022. http://dspace.ncl.res.in:8080/xmlui/handle/20.500.12252/6165.
Pełny tekst źródłaFor thousands of years, scientific discoveries have played a vital role in the progress of human civilization. The discovery of new materials or new scientific phenomena, or an improved understanding of the known phenomena requires exploration through the space available for a given class of molecules (the molecular space). The typical size of molecular space is estimated to be ~1060, which is larger than the number of stars in the observable universe (~1024). Conventional experimental, computational, and algorithmic approaches are inefficient in exploring this vast molecular space. Furthermore, conventional exploration strategies do not take advantage of the large databases available today. On the other hand, machine learning (ML) algorithms can extract hidden knowledge from large datasets. They have shown excellent predictive accuracies in many fields, surpassing the traditional methods. Thus, ML algorithms are promising candidates for developing efficient exploration strategies for the vast molecular space. In this thesis work, we have demonstrated the development of exploration strategies using machine learning algorithms for three different molecular spaces. The first molecular space investigated in this thesis includes battery materials based on phenazine molecules. We have developed an accurate hybrid DFT-ML approach to explore this molecular space. We showed that 2D molecular features are most informative in predicting the redox potential of phenazine derivatives in DME. We also showed that it is possible to develop reasonably accurate machine learning models for complex quantities such as redox potential using small and simple datasets. Next, we investigated different unsupervised machine learning algorithms to explore the molecular space of DNA and proteins to uncover the interactions between them. We have shown that unsupervised machine learning models can discover commonly occurring regulatory modules containing interacting and co-binding transcription factors without prior information on binding activities. Sometimes, in fundamental research, one may encounter the desired property, which cannot be easily computed using existing methodologies. We faced this issue during the investigation of molecular machines. Therefore, we developed an algorithm for quantifying the desired property (i.e., rotational motion) of the ring in the molecular machines. We also investigated linear regression, a machine learning algorithm, during the development. The developed algorithm helped us get an insight into different factors responsible for the rotational directionality of the ring in the rotaxane system. Thus, this thesis work demonstrates the applicability of machine learning and computational tools to the development of efficient exploration strategies for molecular space. This work also shows how to address different issues one may encounter during the development. Furthermore, the specific strategies developed for three molecular spaces are valuable for discovering new molecules and new scientific phenomena. For example, the hybrid DFT-ML approach can help discover promising phenazine derivatives for green energy storage systems such as RFB. The unsupervised machine learning approach developed in this study has the potential to identify genetic determinants of diseases. The algorithm developed for quantifying rotation would help experimentalists develop novel molecular machines having rotational directionality.
AcSIR
Grangier, David. "Machine learning for information retrieval". Lausanne : École polytechnique fédérale de Lausanne, 2008. http://aleph.unisg.ch/volltext/464553_Grangier_Machine_learning_for_information_retrieval.pdf.
Pełny tekst źródłaBaglioni, Cecilia. "Processi Gaussiani e Machine Learning". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20704/.
Pełny tekst źródłaLiao, Yihua. "Machine learning in intrusion detection /". For electronic version search Digital dissertations database. Restricted to UC campuses. Access is free to UC campus dissertations, 2005. http://uclibs.org/PID/11984.
Pełny tekst źródłaStrobl, Carolin. "Statistical Issues in Machine Learning". Diss., lmu, 2008. http://nbn-resolving.de/urn:nbn:de:bvb:19-89043.
Pełny tekst źródłaStendahl, Jonas, i Johan Arnör. "Gesture Keyboard USING MACHINE LEARNING". Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-157141.
Pełny tekst źródłaMarknaden för mobila enheter expanderar kraftigt. Inmatning är en viktig del vid användningen av sådana produkter och en inmatningsmetod som är smidig och snabb är därför mycket intressant. Ett tangentbord för gester erbjuder användaren möjligheten att skriva genom att dra fingret över bokstäverna i det önskade ordet. I denna studie undersöks om tangentbord för gester kan förbättras med hjälp av maskininlärning. Ett tangentbord som använde en Multilayer Perceptron med backpropagation utvecklades och utvärderades. Resultaten visar att den undersökta implementationen inte är en optimal lösning på problemet att känna igen ord som matas in med hjälp av gester.
Bergkvist, Markus, i Tobias Olandersson. "Machine learning in simulated RoboCup". Thesis, Blekinge Tekniska Högskola, Institutionen för programvaruteknik och datavetenskap, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3827.
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