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Least Action Principles and Well-Posed Learning Problems

Least Action Principles and Well-Posed Learning Problems

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2252541693

Least Action Principles and Well-Posed Learning Problems

About this item

Full title

Least Action Principles and Well-Posed Learning Problems

Author / Creator

Publisher

Ithaca: Cornell University Library, arXiv.org

Journal title

arXiv.org, 2019-07

Language

English

Formats

Publication information

Publisher

Ithaca: Cornell University Library, arXiv.org

More information

Scope and Contents

Contents

Machine Learning algorithms are typically regarded as appropriate optimization schemes for minimizing risk functions that are constructed on the training set, which conveys statistical flavor to the corresponding learning problem. When the focus is shifted on perception, which is inherently interwound with time, recent alternative formulations of l...

Alternative Titles

Full title

Least Action Principles and Well-Posed Learning Problems

Authors, Artists and Contributors

Identifiers

Primary Identifiers

Record Identifier

TN_cdi_proquest_journals_2252541693

Permalink

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2252541693

Other Identifiers

E-ISSN

2331-8422

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