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Books > Computing & IT > Applications of computing > Artificial intelligence
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Model Selection and Error Estimation in a Nutshell (Paperback, 1st ed. 2020)
Loot Price: R2,927
Discovery Miles 29 270
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Model Selection and Error Estimation in a Nutshell (Paperback, 1st ed. 2020)
Series: Modeling and Optimization in Science and Technologies, 15
Expected to ship within 10 - 15 working days
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How can we select the best performing data-driven model? How can we
rigorously estimate its generalization error? Statistical learning
theory answers these questions by deriving non-asymptotic bounds on
the generalization error of a model or, in other words, by upper
bounding the true error of the learned model based just on
quantities computed on the available data. However, for a long
time, Statistical learning theory has been considered only an
abstract theoretical framework, useful for inspiring new learning
approaches, but with limited applicability to practical problems.
The purpose of this book is to give an intelligible overview of the
problems of model selection and error estimation, by focusing on
the ideas behind the different statistical learning theory
approaches and simplifying most of the technical aspects with the
purpose of making them more accessible and usable in practice. The
book starts by presenting the seminal works of the 80's and
includes the most recent results. It discusses open problems and
outlines future directions for research.
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