Tesi sul tema "Classification based on generative models"
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Cazzanti, Luca. "Generative models of similarity-based classification /". Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/5905.
Testo completoLjungberg, Lucas. "Using unsupervised classification with multiple LDA derived models for text generation based on noisy and sensitive data". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-255010.
Testo completoAtt skapa modeller som genererar kontextuella svar på frågor är ett svårt problem från början, någonting som blir än mer svårt när tillgänglig data innehåller både brus och känslig information. Det är både viktigt och av stort intresse att hitta modeller och metoder som kan hantera dessa svårigheter så att även problematisk data kan användas produktivt.Detta examensarbete föreslår en modell baserat på ett par samarbetande Topic Models (ämnesbaserade modeller) med skiljande ansvarsområden (LDA och GSDMM) för att underlätta de problematiska egenskaperna av datan. Modellen testas på ett verkligt dataset med dessa svårigheter samt ett dataset utan dessa. Målet är att 1) inspektera båda ämnesmodellernas beteende för att se om dessa kan representera datan på ett sådant sätt att andra modeller kan använda dessa som indata eller utdata och 2) förstå vilka av dessa svårigheter som kan hanteras som följd.Resultaten visar att ämnesmodellerna kan representera semantiken och betydelsen av dokument bra nog för att producera välartad indata för andra modeller. Denna representation kan även hantera stora ordlistor och brus i texten. Resultaten visar även att ämnesgrupperingen av responsdatan är godartad nog att användas som mål för klassificeringsmodeller sådant att korrekta meningar kan genereras som respons.
Malazizi, Ladan. "Development of Artificial Intelligence-based In-Silico Toxicity Models. Data Quality Analysis and Model Performance Enhancement through Data Generation". Thesis, University of Bradford, 2008. http://hdl.handle.net/10454/4262.
Testo completoBornelöv, Susanne. "Rule-based Models of Transcriptional Regulation and Complex Diseases : Applications and Development". Doctoral thesis, Uppsala universitet, Beräknings- och systembiologi, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-230159.
Testo completoHaghebaert, Marie. "Outils et méthodes pour la modélisation de la dynamique des écosystèmes microbiens complexes à partir d'observations expérimentales temporelles : application à la dynamique du microbiote intestinal". Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASM036.
Testo completoThis thesis stems from the European project Homo.symbiosus, which investigates the equilibrium transitions of interactions between the host and its intestinal microbiota. To study these transitions, we pursue two directions: the mechanistic modeling of host-microbiota interactions and the analysis of temporal microbial count data.We enriched and simulated a deterministic model of the intestinal crypt using the EDK numerical scheme, particularly studying the impact of different parameters using the Morris Elementary Effects method. This model proved capable of simulating, on one hand, symbiotic and dysbiotic interaction states and, on the other hand, transition scenarios between states of dysbiosis and symbiosis.In parallel, a compartmental ODE model of the colon, inspired by existing studies, was developed and coupled with the crypt model. The thesis contributed to the enhancement of bacterial metabolism modeling and the modeling of innate immunity at the scale of the intestinal mucosa. A numerical exploration allowed us to assess the influence of diet on the steady state of the model and to study the effect of a pathological scenario by mimicking a breach in the epithelial barrier.Furthermore, we developed an approach to analyze microbial data aimed at assessing the deviation of microbial ecosystems undergoing significant environmental disturbances compared to a reference state. This method, based on DMM classification, enables the study of ecosystem equilibrium transitions in cases with few individuals and few time points. Moreover, a curve classification method using the SBM model was applied to investigate the effects of various disturbances on the microbial ecosystem; the results from this study were used to enrich the host-microbiota interaction model
Müller, Richard. "Software Visualization in 3D". Doctoral thesis, Universitätsbibliothek Leipzig, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-164699.
Testo completoOzer, Gizem. "Fuzzy Classification Models Based On Tanaka". Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12610785/index.pdf.
Testo completos Fuzzy Linear Regression (FLR) approach, and an improvement of an existing one, Improved Fuzzy Classifier Functions (IFCF). Tanaka&rsquo
s FLR approach is a well known fuzzy regression technique used for the prediction problems including fuzzy type of uncertainty. In the first part of the study, three alternative approaches are presented, which utilize the FLR approach for a particular customer satisfaction classification problem. A comparison of their performances and their applicability in other cases are discussed. In the second part of the study, the improved IFCF method, Nonparametric Improved Fuzzy Classifier Functions (NIFCF), is presented, which proposes to use a nonparametric method, Multivariate Adaptive Regression Splines (MARS), in clustering phase of the IFCF method. NIFCF method is applied on three data sets, and compared with Fuzzy Classifier Function (FCF) and Logistic Regression (LR) methods.
Elzobi, Moftah M. [Verfasser]. "Unconstrained recognition of offline Arabic handwriting using generative and discriminative classification models / Moftah M. Elzobi". Magdeburg : Universitätsbibliothek, 2017. http://d-nb.info/1135662185/34.
Testo completoSantiago, Dionny. "A Model-Based AI-Driven Test Generation System". FIU Digital Commons, 2018. https://digitalcommons.fiu.edu/etd/3878.
Testo completoBirks, Daniel J. "Computational Agent-Based Models of Offending: Assessing the Generative Sufficiency of Opportunity-Based Explanations of the Crime Event". Thesis, Griffith University, 2012. http://hdl.handle.net/10072/367327.
Testo completoThesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Criminology and Criminal Justice
Arts, Education and Law
Full Text
Peitz, Stephan Verfasser], Hermann [Akademischer Betreuer] [Ney e Alexandre [Akademischer Betreuer] Allauzen. "Generative Training and Smoothing of Hierarchical Phrase-Based Translation Models / Stephan Peitz ; Hermann Ney, Alexandre Allauzen". Aachen : Universitätsbibliothek der RWTH Aachen, 2017. http://d-nb.info/1162063742/34.
Testo completoPeitz, Stephan [Verfasser], Hermann [Akademischer Betreuer] Ney e Alexandre [Akademischer Betreuer] Allauzen. "Generative Training and Smoothing of Hierarchical Phrase-Based Translation Models / Stephan Peitz ; Hermann Ney, Alexandre Allauzen". Aachen : Universitätsbibliothek der RWTH Aachen, 2017. http://d-nb.info/1162063742/34.
Testo completoTOMA, ANDREA. "PHY-layer Security in Cognitive Radio Networks through Learning Deep Generative Models: an AI-based approach". Doctoral thesis, Università degli studi di Genova, 2020. http://hdl.handle.net/11567/1003576.
Testo completoSingh, Vivek Kumar. "Segmentation and classification of multimodal medical images based on generative adversarial learning and convolutional neural networks". Doctoral thesis, Universitat Rovira i Virgili, 2019. http://hdl.handle.net/10803/668445.
Testo completoEl objetivo principal de esta tesis es crear un sistema CAD avanzado para cualquier tipo de modalidad de imagen médica con altas tasas de sensibilidad y especificidad basadas en técnicas de aprendizaje profundo. Más concretamente, queremos mejorar el método automático de detección de las regiones de interés (ROI), que son áreas de la imagen que contienen posibles tejidos enfermos, así como la segmentación de los hallazgos (delimitación de la frontera) y, en definitiva, una predicción del diagnóstico más adecuado (clasificación). En esta tesis nos centramos en diversos campos, que incluyen mamografías y ecografías para diagnosticar un cáncer de mama, análisis de lesiones de la piel en imágenes dermoscòpiques y inspección del fondo de la retina para evitar la retinopatía diabética
The main aim of this thesis is to create an advanced CAD system for any type of medical image modality with high sensitivity and specificity rates based on deep learning techniques. More specifically, we want to improve the automatic method of detection of Regions of Interest (ROI), which are areas of the image that contain possible ill tissues, as well as segmentation of the findings (delimitation with a boundary), and ultimately, a prediction of a most suitable diagnose (classification). In this thesis, we focus on several topics including mammograms and ultrasound images to diagnose breast cancer, skin lesions analysis in dermoscopic images and retinal fundus images examination to avoid diabetic retinopathy.
Frisk, Christoffer. "Automated protein-family classification based on hidden Markov models". Thesis, Uppsala universitet, Bioinformatik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-252372.
Testo completoGhosh, Anubhab. "Normalizing Flow based Hidden Markov Models for Phone Recognition". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-286594.
Testo completoUppgiften för fonemigenkänning är en grundläggande uppgift i taligenkänning och tjänar ofta en kritisk roll i benchmarkingändamål. Forskare har använt en mängd olika modeller som använts tidigare för att hantera denna uppgift genom att använda både generativa och diskriminerande inlärningssätt. Bland dem är generativa tillvägagångssätt som användning av Gaussian-blandnings modellbaserade dolda Markov-modeller alltid föredragna på grund av deras matematiska spårbarhet. Men användningen av generativa modeller som dolda Markov-modeller och dess hybridvarianter är inte längre på mode på grund av en stor lutning till diskriminerande inlärningsmetoder, som har visat sig fungera bättre. Den enda nackdelen är att dessa tillvägagångssätt inte alltid säkerställer matematisk spårbarhet eller konvergensgarantier i motsats till deras generativa motsvarigheter. Således var forskningsproblemet att undersöka om det kan finnas en process för att förstärka modelleringsförmågan hos generativa modeller med hjälp av ett slags neurala nätverksbaserade arkitekturer som samtidigt kunde visa sig matematiskt spårbart och uttrycksfullt. Normaliseringsflöden är en klass generativa modeller som nyligen har fått mycket uppmärksamhet inom området för densitetsberäkning och erbjuder en metod för exakt sannolikhetsberäkning och slutsats. I detta projekt användes några få varianter av Normaliserande flödesbaserade dolda Markov-modeller för uppgiften att fonemigenkänna i TIMIT-datasatsen. Det visade sig att dessa modeller och deras blandningsmodellvarianter överträffade klassiska generativa modellvarianter som Gaussiska blandningsmodeller. Ett beslutssmältningsstrategi med klassiska Gaussiska och Normaliserande flödesbaserade blandningar visade konkurrenskraftiga resultat jämfört med diskriminerande inlärningsmetoder. Ytterligare analys baserat på klasser av talsignaler utfördes för att jämföra de generativa modellerna som användes. Dessutom genomfördes en studie av robustheten hos dessa algoritmer till bullriga talförhållanden.
Arastuie, Makan. "Generative Models of Link Formation and Community Detection in Continuous-Time Dynamic Networks". University of Toledo / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1596718772873086.
Testo completoLiu, Dan. "Tree-based Models for Longitudinal Data". Bowling Green State University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1399972118.
Testo completoChen, Xiujuan. "Computational Intelligence Based Classifier Fusion Models for Biomedical Classification Applications". Digital Archive @ GSU, 2007. http://digitalarchive.gsu.edu/cs_diss/26.
Testo completoAzeraf, Elie. "Classification avec des modèles probabilistes génératifs et des réseaux de neurones. Applications au traitement des langues naturelles". Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. https://theses.hal.science/tel-03880848.
Testo completoMany probabilistic models have been neglected for classification tasks with supervised learning for several years, as the Naive Bayes or the Hidden Markov Chain. These models, called generative, are criticized because the induced classifier must learn the observations' law. This problem is too complex when the number of observations' features is too large. It is especially the case with Natural Language Processing tasks, as the recent embedding algorithms convert words in large numerical vectors to achieve better scores.This thesis shows that every generative model can define its induced classifier without using the observations' law. This proposition questions the usual categorization of the probabilistic models and classifiers and allows many new applications. Therefore, Hidden Markov Chain can be efficiently applied to Chunking and Naive Bayes to sentiment analysis.We go further, as this proposition allows to define the classifier induced from a generative model with neural network functions. We "neuralize" the models mentioned above and many of their extensions. Models so obtained allow to achieve relevant scores for many Natural Language Processing tasks while being interpretable, able to require little training data, and easy to serve
Nepali, Anjeev. "County Level Population Estimation Using Knowledge-Based Image Classification and Regression Models". Thesis, University of North Texas, 2010. https://digital.library.unt.edu/ark:/67531/metadc30498/.
Testo completoLindeman, Victor. "An Analysis of Cloud-Based Machine Learning Models for Traffic-Sign Classification". Thesis, Linköpings universitet, Kommunikations- och transportsystem, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-160022.
Testo completoBjöörn, Anton. "Employing a Transformer Language Model for Information Retrieval and Document Classification : Using OpenAI's generative pre-trained transformer, GPT-2". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281766.
Testo completoInformationsflödet på Internet fortsätter att öka vilket gör det allt lättare att missa viktiga nyheter som inte intresserar en stor mängd människor. För att bekämpa detta problem behövs allt mer sofistikerade informationssökningsmetoder. Förtränade transformermodeller har sedan ett par år tillbaka tagit över som de mest framstående neurala nätverken för att hantera text. Det här arbetet undersöker hur väl en sådan språkmodell, Open AIs General Pre-trained Transformer 2 (GPT-2), kan generalisera från att generera text till att användas för informationssökning och klassificering av texter. För att utvärdera detta jämförs en transformerbaserad modell med en mer traditionell Term Frequency- Inverse Document Frequency (TF-IDF) vektoriseringsmodell. Målet är att klargöra hur användbara förtränade transformermodeller faktiskt är i skapandet av specialiserade informationssökningssystem. Den minsta versionen av språkmodellen GPT-2 anpassas och tränas om till att ranka och klassificera nyhetsartiklar, skrivna på engelska, och uppnår liknande prestanda som den TF-IDF baserade modellen. Den GPT-2 baserade modellen hade i genomsnitt 0.74 procentenheter högre Normalized Discounted Cumulative Gain (NDCG) men provstorleken var ej stor nog för att ge dessa resultat hög statistisk säkerhet.
Kaden, Marika. "Integration of Auxiliary Data Knowledge in Prototype Based Vector Quantization and Classification Models". Doctoral thesis, Universitätsbibliothek Leipzig, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-206413.
Testo completoGoodman, Genghis. "A Machine Learning Approach to Artificial Floorplan Generation". UKnowledge, 2019. https://uknowledge.uky.edu/cs_etds/89.
Testo completoLlerena, Julissa Giuliana Villanueva. "Multi-label classification based on sum-product networks". Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/45/45134/tde-08122017-100124/.
Testo completoA classificação Multi-Rótulo consiste em aprender uma função que seja capaz de mapear um objeto para um conjunto de rótulos relevantes. Ela possui aplicações como associação de genes com funções biológicas, classificação semântica de cenas e categorização de texto. A classificação tradicional, de rótulo único é, portanto, um caso particular da Classificação Multi-Rótulo, onde cada objeto está associado com exatamente um rótulo. Uma abordagem bem sucedida para classificação é obter um modelo probabilístico da relação entre atributos do objeto e rótulos. Esse modelo pode então ser usado para classificar objetos, encon- trando a predição mais provável por meio da probabilidade marginal ou a explicação mais provavél dos rótulos dados os atributos. Dependendo da família de modelos probabilísticos escolhidos, tais inferências podem ser intratáveis quando o número de rótulos é grande. As redes Soma-Produto (SPN, do inglês Sum Product Network) são modelos probabilísticos profundos, que permitem inferência marginal tratável. No entanto, como em muitos outros modelos probabilísticos, a inferência da explicação mais provavél é NP-difícil. Embora SPNs já tenham sido usadas com sucesso para tarefas de classificação tradicionais, não existe investigação aprofundada no uso de SPNs para classificação Multi-Rótulo. Neste trabalho, investigamos o uso de SPNs para classificação Multi-Rótulo. Comparamos vários algoritmos de aprendizado de SPNs combinados com diferentes abordagens propostos para classi- ficação. Mostramos que os classificadores Multi-Rótulos baseados em SPN são competitivos contra classificadores estado-da-arte, como Random k-Labelsets usando Máquinas de Suporte Vetorial e inferência exata da explicação mais provavél em CutNets, em uma coleção de conjuntos de dados de referência.
Zhu, Jia Jun. "A language for financial chart patterns and template-based pattern classification". Thesis, University of Macau, 2018. http://umaclib3.umac.mo/record=b3950603.
Testo completoAl, Tobi Amjad Mohamed. "Anomaly-based network intrusion detection enhancement by prediction threshold adaptation of binary classification models". Thesis, University of St Andrews, 2018. http://hdl.handle.net/10023/17050.
Testo completoKrinner, Axel. "Spherical Individual Cell-Based Models". Doctoral thesis, Universitätsbibliothek Leipzig, 2010. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-38817.
Testo completoGillies, Robert Robertson. "A physically based land-use classification scheme using remote solar and thermal infrared measurements suitable for describing urbanization". Thesis, University of Newcastle Upon Tyne, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.480879.
Testo completoSiefkes, Christian. "An incrementally trainable statistical approach to information extraction based on token classification and rich context models". [S.l.] : [s.n.], 2007. http://www.diss.fu-berlin.de/2007/173/index.html.
Testo completoAnese, Gianluca <1995>. "Explanatory power of GARCH models using news-based investor sentiment: Applications of LSTM networks for text classification". Master's Degree Thesis, Università Ca' Foscari Venezia, 2020. http://hdl.handle.net/10579/16940.
Testo completoAla'raj, Maher A. "A credit scoring model based on classifiers consensus system approach". Thesis, Brunel University, 2016. http://bura.brunel.ac.uk/handle/2438/13669.
Testo completoAlam, Fahim Irfan. "Deep Feature Learning for Spectral-Spatial Classification of Hyperspectral Remote Sensing Images". Thesis, Griffith University, 2019. http://hdl.handle.net/10072/386535.
Testo completoThesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Info & Comm Tech
Science, Environment, Engineering and Technology
Full Text
Lisena, Pasquale. "Knowledge-based music recommendation : models, algorithms and exploratory search". Electronic Thesis or Diss., Sorbonne université, 2019. http://www.theses.fr/2019SORUS614.
Testo completoRepresenting the information about music is a complex activity that involves different sub-tasks. This thesis manuscript mostly focuses on classical music, researching how to represent and exploit its information. The main goal is the investigation of strategies of knowledge representation and discovery applied to classical music, involving subjects such as Knowledge-Base population, metadata prediction, and recommender systems. We propose a complete workflow for the management of music metadata using Semantic Web technologies. We introduce a specialised ontology and a set of controlled vocabularies for the different concepts specific to music. Then, we present an approach for converting data, in order to go beyond the librarian practice currently in use, relying on mapping rules and interlinking with controlled vocabularies. Finally, we show how these data can be exploited. In particular, we study approaches based on embeddings computed on structured metadata, titles, and symbolic music for ranking and recommending music. Several demo applications have been realised for testing the previous approaches and resources
Saleh, Alraimi Adel. "Development of New Models for Vision-Based Human Activity Recognition". Doctoral thesis, Universitat Rovira i Virgili, 2019. http://hdl.handle.net/10803/670893.
Testo completoLos métodos de reconocimiento de acciones permiten que los sistemas inteligentes reconozcan acciones humanas en videos de la vida cotidiana. No obstante, muchos métodos de reconocimiento de acciones dan tasas notables de error de clasificación debido a las grandes variaciones dentro de los videos de la misma clase y los cambios en el punto de vista, la escala y el fondo. Para reducir la clasificación errónea, Łproponemos un nuevo método de representación de video que captura la evolución temporal de la acción que ocurre en el video completo, un nuevo método para la segmentación de manos y un nuevo método para el reconocimiento de actividades humanas en imágenes fijas.
Action recognition methods enable intelligent systems to recognize human actions in daily life videos. However, many action recognition methods give noticeable misclassification rates due to the big variations within the videos of the same class, and the changes in viewpoint, scale and background. To reduce the misclassification rate, we propose a new video representation method that captures the temporal evolution of the action happening in the whole video, a new method for human hands segmentation and a new method for human activity recognition in still images.
Navas, Juan Moreno. "Three-dimensional hydrodynamic models coupled with GIS-based neuro-fuzzy classification for assessing environmental vulnerability of marine cage aquaculture". Thesis, University of Stirling, 2010. http://hdl.handle.net/1893/2580.
Testo completoThomas, Anita. "Classification of Man-made Urban Structures from Lidar Point Clouds with Applications to Extrusion-based 3-D City Models". The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1429484410.
Testo completoSzeptycki, Przemyslaw. "Processing and analysis of 2.5D face models for non-rigid mapping based face recognition using differential geometry tools". Phd thesis, Ecole Centrale de Lyon, 2011. http://tel.archives-ouvertes.fr/tel-00675988.
Testo completoHameed, Khurram. "Computer vision based classification of fruits and vegetables for self-checkout at supermarkets". Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2022. https://ro.ecu.edu.au/theses/2519.
Testo completoPathni, Charu. "Round-trip engineering concept for hierarchical UML models in AUTOSAR-based safety projects". Master's thesis, Universitätsbibliothek Chemnitz, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-187153.
Testo completoKoban, Martin. "Machine learning models for quantifying phenotypic signatures of cancer cells based on transcriptomic and epigenomic data". Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2020. http://www.nusl.cz/ntk/nusl-433123.
Testo completoFischer, Marco. "A formal fault model for component based models of embedded systems". Dresden TUDpress, 2006. http://deposit.d-nb.de/cgi-bin/dokserv?id=2960240&prov=M&dok_var=1&dok_ext=htm.
Testo completoChali, Samy. "Robustness Analysis of Classifiers Against Out-of-Distribution and Adversarial Inputs". Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPAST012.
Testo completoMany issues addressed by AI involve the classification of complex input data that needs to be separated into different classes. The functions that transform the complex input values into a simpler, linearly separable space are achieved either through learning (deep convolutional networks) or by projecting into a high-dimensional space to obtain a 'rich' non-linear representation of the inputs, followed by a linear mapping between the high-dimensional space and the output units, as used in Support Vector Machines (Vapnik's work 1966-1995). The thesis aims to create an optimized, generic architecture capable of preprocessing data to prepare them for classification with minimal operations required. Additionally, this architecture aims to enhance the model's autonomy by enabling continuous learning, robustness to corrupted data, and the identification of data that the model cannot process
Kaden, Marika [Verfasser], Martin [Akademischer Betreuer] Bogdan, Thomas [Akademischer Betreuer] Villmann e John A. [Gutachter] Lee. "Integration of Auxiliary Data Knowledge in Prototype Based Vector Quantization and Classification Models / Marika Kaden ; Gutachter: John A. Lee ; Martin Bogdan, Thomas Villmann". Leipzig : Universitätsbibliothek Leipzig, 2016. http://d-nb.info/1240482809/34.
Testo completoRailsback, Steven, Daniel Ayllón, Uta Berger, Volker Grimm, Steven Lytinen, Colin Sheppard e Jan C. Thiele. "Improving Execution Speed of Models Implemented in NetLogo". JASSS, 2016. https://tud.qucosa.de/id/qucosa%3A30227.
Testo completoHatefi, Armin. "Mixture model analysis with rank-based samples". Statistica Sinica, 2013. http://hdl.handle.net/1993/23849.
Testo completoYoshida, Masayuki, Masami Morooka, Shuji Tanaka e Manabu Takahashi. "Formation mechanism of plateau, rapid fall and tail in phosphorus diffusion profile in silicon based on the pair diffusion models of vacancy mechanism and interstitial mechanism". Diffusion fundamentals 2 (2005) 62, S. 1-2, 2005. https://ul.qucosa.de/id/qucosa%3A14396.
Testo completoMobasher, Barzin. "Development of Design Procedures for Flexural Applications of Textile Composite Systems Based on Tension Stiffening Models". Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2011. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-77984.
Testo completoRodriguez, Johnnatan, Kevin Hoefer, Andre Haelsig e Peter Mayr. "Functionally Graded SS 316L to Ni-Based Structures Produced by 3D Plasma Metal Deposition". MDPI AG, 2019. https://monarch.qucosa.de/id/qucosa%3A34781.
Testo completo