English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Falsification and future performance

Balduzzi, D. (2013). Falsification and future performance. In D. Dowe (Ed.), Algorithmic Probability and Friends. Bayesian Prediction and Artificial Intelligence (pp. 65-78). Berlin, Germany: Springer.

Item is

Files

show Files

Locators

show
hide
Description:
-
OA-Status:

Creators

show
hide
 Creators:
Balduzzi, D1, Author           
Affiliations:
1Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, DE, ou_1497647              

Content

show
hide
Free keywords: -
 Abstract: We information-theoretically reformulate two measures of capacity from statistical learning theory: empirical VC-entropy and empirical Rademacher complexity. We show these capacity measures count the number of hypotheses about a dataset that a learning algorithm falsies when it nds the classier in its repertoire minimizing empirical risk. It then follows from that the future performance of predictors on unseen data is controlled in part by how many hypotheses the learner falsies. As a corollary we show that empirical VC-entropy quanties the message length of the true hypothesis in the optimal code of a particular probability distribution, the so-called actual repertoire.

Details

show
hide
Language(s):
 Dates: 2013
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: Balduzzi2011_3
DOI: 10.1007/978-3-642-44958-1_5
 Degree: -

Event

show
hide
Title: Solomonoff 85th Memorial Conference
Place of Event: Melbourne, Australia
Start-/End Date: 2011-12

Legal Case

show

Project information

show

Source 1

show
hide
Title: Algorithmic Probability and Friends. Bayesian Prediction and Artificial Intelligence
Source Genre: Proceedings
 Creator(s):
Dowe, DL, Editor
Affiliations:
-
Publ. Info: Berlin, Germany : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 65 - 78 Identifier: ISBN: 978-3-642-44957-4

Source 2

show
hide
Title: Lecture Notes in Computer Science
Source Genre: Series
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 7070 Sequence Number: - Start / End Page: - Identifier: -