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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 3 Hits 1 – 20 of 55 1 Visualisierungen bei instruktionalen Erklärungen – Eine qualitative Analyse der Bedeutung von Visualisierungen mithilfe von Erklär-Videos am Beispiel des Themas Äquivalenzumformungen Renz (geb. Niebuhr), Karin. - : Freiburg : Pädagogische Hochschule Freiburg, 2021 BASE Show details 2 FALKE: Experiences From Transdisciplinary Educational Research by Fourteen Disciplines Lindl, Alfred; Hilbert, Sven; Schilcher, Anita. - : Frontiers Media SA, 2021 BASE Show details 3 Evaluating Unsupervised Representation Learning for Detecting Stances of Fake News Guderlei, Maike; Aßenmacher, Matthias. - : Ludwig-Maximilians-Universität München, 2020 BASE Show details 4 Flexibilität im Umgang mit mathematischen Situationsstrukturen — Eine Vorstudie für die Entwicklung eines Förderkonzepts zum Lösen additiver Textaufgaben Ufer, Stefan; Gabler, Laura In: Journal für Mathematik-Didaktik (2020) BASE Show details 5 Teacher's attention to characteristics of parabola sketches: differences between use of manual and automated analysis Olsher, Shai; Abu Raya, Kholod. - 2020 BASE Show details 6 Seeing the entire picture (STEP): an example-eliciting approach to online formative assessment Yerushalmy, Michal. - 2020 BASE Show details 7 Bilingual math lessons with digital tools: challenges can be door opener to language and technology Müller, Matthias. - 2020 BASE Show details 8 Using silent video tasks for formative assessment Kristinsdóttir, Bjarnheiður (Bea); Hreinsdóttir, Freyja; Lavicza, Zsolt. - 2020 BASE Show details 9 Towards automated grouping: unraveling mathematics teachers’ considerations Abdu, Rotem; Olsher, Shai; Yerushalmy, Michal. - 2020 BASE Show details 10 Automated feedback at task level: error analysis or worked out examples – which type is more effective? Pinkernell, Guido; Gulden, Lukas; Kalz, Marco. - 2020 BASE Show details 11 Bedingte Wahrscheinlichkeit: Bedeutungsbezogene Sprache zum Verbal. der Teil-Ganzes-Relation in Anteilsbildern Post, Monika; Prediger, Susanne. - : Gesellschaft für Didaktik der Mathematik, 2020 BASE Show details 12 Capturing teaching practices in language-responsive mathematics classrooms extending the TRU framework “teaching for robust understanding” to L-TRU Prediger, Susanne; Neugebauer, Philipp. - 2020 BASE Show details 13 Erfahrung und Gewissheit – Orientierungen in den Wissenschaften und im Alltag. IV. Regensburger Symposium vom 24.-26. März 2011 Thim-Mabrey, Christiane; Brack, Matthias. - : Universitätsbibliothek Regensburg, 2020 BASE Show details 14 Verzeitlichung des Unsäglichen Carlé, Martin. - : Humboldt-Universität zu Berlin, 2019 BASE Show details 15 Leistungen im Steuerungskontext: Qualitative und quantitative Einflussgrössen auf mathematische und sprachliche Leistungen Meier, Patrick (Dr. phil). - : Hildesheim : Stiftung Universität Hildesheim, 2019 BASE Show details 16 Syntaktische Komplexität individueller Sprachproduktion bei Denkprozessen zu Bestand und Änderung Prediger, Susanne; Şahin-Gür, Dilan. - : Gesellschaft für Didaktik der Mathematik, 2019 BASE Show details 17 ADMM in Optimization and Control: Algorithm Specialization, Computational Distribution, and the Value of Structure Rey, Felix. - : ETH Zurich, 2018 BASE Show details 18 The dynamic logic of stating and asking Ciardelli, Ivano; Baltag, Alexandru. - : Ludwig-Maximilians-Universität München, 2017 BASE Show details 19 Computational and Statistical Tradeoffs via Data Summarization Lucic, Mario. - : ETH Zurich, 2017 Abstract: The massive growth of modern datasets from different sources such as videos, social networks, and sensor data, coupled with limited resources in terms of time and space, raises challenging questions for existing machine learning algorithms. From the statistical point of view, having access to more data may be viewed as a blessing, as it provides a better view of the underlying (possibly stochastic) processes generating the data. At the same time, it greatly increases the cost of storing, communicating, and processing the data. This interplay between the computational and statistical aspects is one of the key challenges in large-scale machine learning. In this dissertation we propose a general approach for addressing these challenges. We study coresets — succinct, small summaries of large datasets — so that solutions computed on the summary are provably competitive with the solution computed on the full data set. Such coresets can be constructed for a variety of important machine learning problems including k-means, maximum likelihood estimation in mixture models, as well as principal component analysis. In most cases, the resulting coresets are small – their size is independent of the original data set size, and only polynomial in other relevant quantities. Furthermore, due to their strong composability properties, coresets admit both streaming and embarrassingly parallel constructions, which lead to practical implementations in the context of large datasets. Finally, coresets can be efficiently computed for a wide range of non-convex optimization problems. We first derive a practical coreset construction framework for a variety of machine learning problems and provide a survey of the existing results. Then, we prove that small coresets can be efficiently constructed for a wide range of density estimation problems in regular exponential mixture models. We demonstrate that in practice the coreset-based approach improves the running time by several orders of magnitude, while introducing a negligible approximation error. We then investigate the resulting computational and statistical tradeoffs: how to use data as a computational resource when available beyond the sample complexity of the learning task? Instead of ignoring the excess data, we propose a data weakening mechanism which allows one to navigate the tradeoffs. Using k-means clustering as a prototypical unsupervised learning problem, we show how to strategically summarize the data in order to trade-off risk and time when the data is generated by a probabilistic model. Specifically, we show that for a fixed risk (or data size), as the data size increases (resp. risk increases) the running time decreases. We then propose a theoretical setting and a tradeoff navigation algorithm which can achieve such tradeoffs. Finally, we consider the practically relevant problem of outlier detection in large datasets. Due to noise, uncertainty and adversarial behavior, outlying observations are inherent to many real-world problems such as fraud or intrusion detection, activity monitoring, and many others. Scaling outlier detection techniques to massive datasets without sacrificing accuracy is a challenging task. We propose a novel distance-based outlier detection algorithm based on the intuition that outliers have a significant influence on the quality of distance-based clustering solutions. In an extensive experimental evaluation, we show that the proposed approach outperforms other popular distance-based approaches while being several orders of magnitude faster. Keyword: computer science; Coresets; Data processing; info:eu-repo/classification/ddc/4; info:eu-repo/classification/ddc/510; Large-scale Machine Learning; Machine Learning; Mathematics; Mixture Models; Outlier Detection URL: https://hdl.handle.net/20.500.11850/220255 https://doi.org/10.3929/ethz-b-000220255 BASE Hide details 20 Sequential estimation techniques and application to multiple speaker tracking and language modeling Oualil, Youssef. - : Saarländische Universitäts- und Landesbibliothek, 2017 BASE Show details Page: 1 2 3

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