Statistical learning and regularisation for regression: Application to system identification and time series modelling - Softcover
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Doctoral Thesis / Dissertation from the year 1997 in the subject Computer Sciences - Artificial Intelligence, grade: n/a, , language: English, abstract: Unravel the complexities of data-driven modeling and step into the realm where accuracy meets resilience in the face of uncertainty. This groundbreaking work delves deep into the heart of statistical learning and regularization, offering a transformative perspective on regression techniques. Discover how these powerful methods can revolutionize system identification and time series modeling, unlocking unprecedented levels of precision and reliability. Explore cutting-edge approaches to model parameter estimation, fortified by rigorous mathematical frameworks and innovative algorithms designed to combat overfitting and enhance generalization. Witness the evolution of model robustness as regularization techniques are meticulously applied to mitigate the impact of noisy data and spurious correlations. This research bridges the gap between theoretical advancements and practical applications, providing invaluable insights for researchers and practitioners alike. Whether you're grappling with the intricacies of dynamic systems or seeking to forecast future trends, this exploration of statistical learning, regularization, and regression will equip you with the tools and knowledge to conquer even the most challenging modeling tasks. Journey through the landscape of data analysis, guided by a comprehensive treatment of both established techniques and novel methodologies, and emerge with a profound understanding of how to build models that are not only accurate but also resilient and insightful. From the foundations of regression to the frontiers of system identification and time series analysis, this is your compass for navigating the ever-evolving world of data-driven decision-making. Prepare to challenge conventional wisdom, embrace the power of regularization, and unlock the true potential of your data. The exploration of statistical learning and its pivotal role in fortifying regression models within the realms of system identification and time series modeling will empower readers to construct models characterized by unparalleled precision and dependability. By navigating the nuances of parameter estimation and employing advanced regularization strategies, this thesis equips you to develop models robust enough to resist the detrimental impact of noisy data and misleading correlations. It is an essential resource for anyone committed to harnessing the full potential of data for informed decision-making and innovative solutions.
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Statistical learning and regularisation for regression
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Doctoral Thesis / Dissertation from the year 1997 in the subject Computer Sciences - Artificial Intelligence, grade: n/a, , language: English, abstract: This thesis deals with the problem of statistical learning. By 'learning'', we mean a process by which we obtain a model of a phenomenon using data measured on it. We focus on system identification and time series modelling, and our work is naturally slightly influenced by this concern. We will for example evoke only regression estimation, and no classification problem. However, most theoretical and practical aspects presented herein can easily be adapted. We study neural networks models. This research has traditionally been linked to computer science and artificial intelligence. In the last few years however, two distinct lines of thoughts seem to diverge: the first one stays close to the biological origins of the term - we will call it 'neuro-biological'; the second considers neural networks as a model, and studies them from a statistical point of view. Our work concerns the second of these lines. Furthermore, we try to stay independent of any specific application, and keep a general approach to neural networks. We attempt to exhibit the links between neural networks and statistics, and show that neural computation can be naturally placed in the realm of traditional statistics. We thus compare neural networks to other models: linear regression as well as non-parametric estimators. We also consider some of the numerous learning techniques developed for neural models, and try to compare them, from both a theoretical and applied point of view. 164 pp. Englisch. Bestandsnummer des Verkäufers 9783668443204
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Statistical learning and regularisation for regression : Application to system identification and time series modelling
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Doctoral Thesis / Dissertation from the year 1997 in the subject Computer Sciences - Artificial Intelligence, grade: n/a, , language: English, abstract: This thesis deals with the problem of statistical learning. By 'learning'', we mean a process by which we obtain a model of a phenomenon using data measured on it. We focus on system identification and time series modelling, and our work is naturally slightly influenced by this concern. We will for example evoke only regression estimation, and no classification problem. However, most theoretical and practical aspects presented herein can easily be adapted. We study neural networks models. This research has traditionally been linked to computer science and artificial intelligence. In the last few years however, two distinct lines of thoughts seem to diverge: the first one stays close to the biological origins of the term - we will call it 'neuro-biological'; the second considers neural networks as a model, and studies them from a statistical point of view. Our work concerns the second of these lines. Furthermore, we try to stay independent of any specific application, and keep a general approach to neural networks. We attempt to exhibit the links between neural networks and statistics, and show that neural computation can be naturally placed in the realm of traditional statistics. We thus compare neural networks to other models: linear regression as well as non-parametric estimators. We also consider some of the numerous learning techniques developed for neural models, and try to compare them, from both a theoretical and applied point of view. Bestandsnummer des Verkäufers 9783668443204
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Statistical learning and regularisation for regression | Application to system identification and time series modelling
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Taschenbuch. Zustand: Neu. Statistical learning and regularisation for regression | Application to system identification and time series modelling | Cyril Goutte | Taschenbuch | 164 S. | Englisch | 2017 | GRIN Verlag | EAN 9783668443204 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu. Bestandsnummer des Verkäufers 109136305
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