Statistical learning and regularisation for regression: Application to system identification and time series modelling - Softcover
Synopsis
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.
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Statistical learning and regularisation for regression
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Taschenbuch. Condition: 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. Seller Inventory # 9783668443204
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Statistical learning and regularisation for regression : Application to system identification and time series modelling
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Taschenbuch. Condition: 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. Seller Inventory # 9783668443204
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Statistical learning and regularisation for regression | Application to system identification and time series modelling
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Taschenbuch. Condition: 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. Seller Inventory # 109136305