Learning and Generalization provides a formal mathematical theory
addressing intuitive questions of the type: • How does a machine
learn a concept on the basis of examples? • How can a neural
network, after training, correctly predict the outcome of a previously
unseen input? • How much training is required to achieve a given
level of accuracy in the prediction? • How can one identify the
dynamical behaviour of a nonlinear control system by observing its
input-output behaviour over a finite time? The second edition covers
new areas including: • support vector machines; • fat-shattering
dimensions and applications to neural network learning; • learning
with dependent samples generated by a beta-mixing process; •
connections between system identification and learning theory; •
probabilistic solution of 'intractable problems' in robust control and
matrix theory using randomized algorithms. It also contains solutions
to some of the open problems posed in the first edition, while adding
new open problems.
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Produktdetaljer
ISBN
9781447137481
Publisert
2020
Utgave
2. utgave
Utgiver
Vendor
Springer
Språk
Product language
Engelsk
Format
Product format
Digital bok
Forfatter