Compression bound is probably the simplest yet theoretically grounded approach to MS and EE. The Compression bound [1–3] relies on a simple idea: if an algorithm is able to compress the data provided to learn a rule then the algorithm will generalize.
Compression Bound
Oneto L.
2020-01-01
Abstract
Compression bound is probably the simplest yet theoretically grounded approach to MS and EE. The Compression bound [1–3] relies on a simple idea: if an algorithm is able to compress the data provided to learn a rule then the algorithm will generalize.
File in questo prodotto:
| File | Dimensione | Formato | |
|---|---|---|---|
|
Model+Selection+and+Error+Estimation+in+.pdf
accesso chiuso
Descrizione: Contributo in volume
Tipologia:
Documento in versione editoriale
Dimensione
2.17 MB
Formato
Adobe PDF
|
2.17 MB | Adobe PDF | Visualizza/Apri Richiedi una copia |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
