Learning theory: An approximation theory viewpoint
โ Scribed by Felipe Cucker, Ding Xuan Zhou
- Book ID
- 127455485
- Publisher
- Cambridge University Press
- Year
- 2007
- Tongue
- English
- Weight
- 1 MB
- Series
- Cambridge Monographs on Applied and Computational Mathematics
- Edition
- 1
- Category
- Library
- ISBN
- 051127551X
No coin nor oath required. For personal study only.
โฆ Synopsis
The goal of learning theory is to approximate a function from sample values. To attain this goal learning theory draws on a variety of diverse subjects, specifically statistics, approximation theory, and algorithmics. Ideas from all these areas blended to form a subject whose many successful applications have triggered a rapid growth during the last two decades. This is the first book to give a general overview of the theoretical foundations of the subject emphasizing the approximation theory, while still giving a balanced overview. It is based on courses taught by the authors, and is reasonably self-contained so will appeal to a broad spectrum of researchers in learning theory and adjacent fields. It will also serve as an introduction for graduate students and others entering the field, who wish to see how the problems raised in learning theory relate to other disciplines.
๐ SIMILAR VOLUMES
This article focuses on the characterization of two models of concatenated convolutional codes from the perspective of linear systems theory. We present an input-state-output representation of these models and study the conditions for obtaining a minimal input-state-output representation and non-cat