Tracked shipping to Austria with premium packaging for just 3,99 € 

Ship to
Austria
0

My Account

  • argentina
  • chile
  • colombia
  • españa
  • méxico
  • perú
  • estados unidos
  • internacional

menu

Select your country

Americas

Europe

Rest of the world

portada Statistical Learning and Regularisation for Regression: Application to System Identification and Time Series Modelling
Type
Physical Book
Publisher
Author
Language
English
Pages
164
Format
Paperback
ISBN13
9783668443204

Statistical Learning and Regularisation for Regression: Application to System Identification and Time Series Modelling

Cyril Goutte (Author) · Grin Verlag · Paperback

Statistical Learning and Regularisation for Regression: Application to System Identification and Time Series Modelling - Cyril Goutte

New Book Imported to Austria
Delivery: 14 Aug - 18 Aug Shipping: 13 to 14 business days.
58,33 €
Import costs and 10% VAT included in the price ✅
58,33 €

Choose the list to add your product or create one New List

Go to My Wishlists
Calculate shipping cost

Synopsis "Statistical Learning and Regularisation for Regression: Application to System Identification and Time Series Modelling"

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.

Customers reviews

More customer reviews
  • 0% (0)
  • 0% (0)
  • 0% (0)
  • 0% (0)
  • 0% (0)

Frequently Asked Questions about the Book

Is the book original?

All books in our catalog are Original.

In what language is the book written?

The book is written in English.

What is the format of this book?

The binding of this edition is Paperback.

Questions and Answers about the Book

Do you have a question about the book? Login to be able to add your own question.

Opinions about Bookdelivery

More customer reviews