### Abstract

Language | English |
---|---|

Title of host publication | Algorithmic Learning Theory - 26th International Conference, ALT 2015 |

Publisher | Springer/Verlag |

Pages | 379-394 |

Number of pages | 16 |

Volume | 9355 |

ISBN (Print) | 9783319244853 |

DOIs | |

State | Published - 2015 |

Externally published | Yes |

Event | 26th International Conference on Algorithmic Learning Theory (ALT 2015) - Banff, Canada Duration: 4 Oct 2015 → 6 Oct 2015 |

### Publication series

Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
---|---|

Volume | 9355 |

ISSN (Print) | 0302-9743 |

ISSN (Electronic) | 1611-3349 |

### Conference

Conference | 26th International Conference on Algorithmic Learning Theory (ALT 2015) |
---|---|

Country | Canada |

City | Banff |

Period | 4/10/15 → 6/10/15 |

### Fingerprint

### Cite this

*Algorithmic Learning Theory - 26th International Conference, ALT 2015*(Vol. 9355, pp. 379-394). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9355). Springer/Verlag. DOI: 10.1007/978-3-319-24486-0_25

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*Algorithmic Learning Theory - 26th International Conference, ALT 2015.*vol. 9355, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9355, Springer/Verlag, pp. 379-394, 26th International Conference on Algorithmic Learning Theory (ALT 2015), Banff, Canada, 4/10/15. DOI: 10.1007/978-3-319-24486-0_25

**Two Problems for Sophistication.** / Bloem, Peter; de Rooij, Steven; Adriaans, Pieter.

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Two Problems for Sophistication

AU - Bloem,Peter

AU - de Rooij,Steven

AU - Adriaans,Pieter

PY - 2015

Y1 - 2015

N2 - Kolmogorov complexity measures the amount of information in data, but does not distinguish structure from noise. Kolmogorov’s definition of the structure function was the first attempt to measure only the structural information in data, by measuring the complexity of the smallest model that allows for optimal compression of the data. Since then, many variations of this idea have been proposed, for which we use sophistication as an umbrella term. We describe two fundamental problems with existing proposals, showing many of them to be unsound. Consequently, we put forward the view that the problem is fundamental: it may be impossible to objectively quantify the sophistication.

AB - Kolmogorov complexity measures the amount of information in data, but does not distinguish structure from noise. Kolmogorov’s definition of the structure function was the first attempt to measure only the structural information in data, by measuring the complexity of the smallest model that allows for optimal compression of the data. Since then, many variations of this idea have been proposed, for which we use sophistication as an umbrella term. We describe two fundamental problems with existing proposals, showing many of them to be unsound. Consequently, we put forward the view that the problem is fundamental: it may be impossible to objectively quantify the sophistication.

UR - http://www.scopus.com/inward/record.url?scp=84945943708&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84945943708&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-24486-0_25

DO - 10.1007/978-3-319-24486-0_25

M3 - Conference contribution

SN - 9783319244853

VL - 9355

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 379

EP - 394

BT - Algorithmic Learning Theory - 26th International Conference, ALT 2015

PB - Springer/Verlag

ER -