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Rocchetta et al., 2023 - Google Patents

A survey on scenario theory, complexity, and compression-based learning and generalization

Rocchetta et al., 2023

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Document ID
10801333161122219934
Author
Rocchetta R
Mey A
Oliehoek F
Publication year
Publication venue
IEEE Transactions on Neural Networks and Learning Systems

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Snippet

This work investigates formal generalization error bounds that apply to support vector machines (SVMs) in realizable and agnostic learning problems. We focus on recently observed parallels between probably approximately correct (PAC)-learning bounds, such as …
Continue reading at papers.ssrn.com (PDF) (other versions)

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