Learning and generalisation. With applications to neural networks.
type: Item
instance of: scholarly article
title: Learning and generalisation. With applications to neural networks.
zbMATH Open document ID: 1008.68102
published in: Communications and Control Engineering
publication date: 2003-02-03T00:00:00Z
review text: In this second edition [for a review of the first one, see (1997; Zbl 0928.68061)], according to its preface, two main innovations are to be found: First, the hypothesis of independent and identically distributed samples of the learning algorithm is weakened to a mixing one which is seen to appear in certain Markov processes. Second, applications in systems science via the utilization of randomized algorithms is twofold: a) Many synthesis algorithms which are NP-hard in a deterministic context become P hard when randomized. b) Identification results provide finite-time estimates with the methods of this book while only asymptotic results are obtained in the classical way; this is useful in design when identification and control are combined. Some chapters have been modified to take into account of recent advances.
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 68-02
Mathematics Subject Classification ID: 68Q32
Mathematics Subject Classification ID: 93E35
Mathematics Subject Classification ID: 93B40
Mathematics Subject Classification ID: 93E12
Mathematics Subject Classification ID: 93B50
zbMATH DE Number: 1862808
zbMATH Keywords: mixing property
zbMATH Keywords: statistical learning
zbMATH Keywords: neural network
zbMATH Keywords: empirical means
zbMATH Keywords: identification
zbMATH Keywords: synthesis algorithms
zbMATH Keywords: NP-hard
zbMATH Keywords: finite-time estimates
author: M. Vidyasagar
reviewed by: A. Akutowicz
MaRDI profile type: Publication
description: scientific article; zbMATH DE number 1862808