Statistical learning and regularisation for regression

Application to system identification and time series modelling. Dissertationsschrift

Szerző: Cyril Goutte

Nyelv:

Kötés: Puha kötésű

Kiadó: Grin Publishing

Elérhetőség: Beszállítói készleten

Küldés 5-8 napon belül

17 790 Ft

Doctoral Thesis / Dissertation from the year 1997 in the subject Engineering - Artificial Intelligen...

Információk a könyvről

Szerző

Cyril Goutte

Nyelv

Angol

Kötés

Könyv - Puha kötésű

Kiadva

2017

oldal

164

EAN

9783668443204

ISBN

3668443203

Enbook ID

16404246

Kiadó

Grin Publishing

Súly

245

Méretek

148 x 210 x 11

Teljes leírás

Doctoral Thesis / Dissertation from the year 1997 in the subject Engineering - 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

Application to system identification and time series modelling. Dissertationsschrift

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Teljes leírás

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