### Abstract

Language | English |
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Title of host publication | Proceedings of the 16th International Conference Algorithmic Learning Theory 2005 |

Place of Publication | Berlin Heidelberg |

Pages | 414-428 |

Number of pages | 15 |

Volume | 3734 |

DOIs | |

State | Published - 31 Dec 2005 |

Event | Proceedings of the 16th International Conference Algorithmic Learning Theory 2005 - Singapore, October 8-11, 2005 Duration: 31 Dec 2005 → … |

### Publication series

Name | Lecture Notes in Computer Science |
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### Conference

Conference | Proceedings of the 16th International Conference Algorithmic Learning Theory 2005 |
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Period | 31/12/05 → … |

### Fingerprint

### Cite this

*Proceedings of the 16th International Conference Algorithmic Learning Theory 2005*(Vol. 3734, pp. 414-428). (Lecture Notes in Computer Science). Berlin Heidelberg. DOI: 10.1007/11564089_32

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*Proceedings of the 16th International Conference Algorithmic Learning Theory 2005.*vol. 3734, Lecture Notes in Computer Science, Berlin Heidelberg, pp. 414-428, Proceedings of the 16th International Conference Algorithmic Learning Theory 2005, 31/12/05. DOI: 10.1007/11564089_32

**Monotone conditional complexity bounds on future prediction errors.** / Chernov, Alexey; Hutter, Marcus.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution with ISSN or ISBN

TY - GEN

T1 - Monotone conditional complexity bounds on future prediction errors

AU - Chernov,Alexey

AU - Hutter,Marcus

PY - 2005/12/31

Y1 - 2005/12/31

N2 - We bound the future loss when predicting any (computably) stochastic sequence online. Solomonoff finitely bounded the total deviation of his universal predictor M from the true distribution m by the algorithmic complexity of m. Here we assume we are at a time t>1 and already observed x=x 1...xt. We bound the future prediction performance on xt+1xt+2... by a new variant of algorithmic complexity of m given x, plus the complexity of the randomness deficiency of x. The new complexity is monotone in its condition in the sense that this complexity can only decrease if the condition is prolonged. We also briefly discuss potential generalizations to Bayesian model classes and to classification problems.

AB - We bound the future loss when predicting any (computably) stochastic sequence online. Solomonoff finitely bounded the total deviation of his universal predictor M from the true distribution m by the algorithmic complexity of m. Here we assume we are at a time t>1 and already observed x=x 1...xt. We bound the future prediction performance on xt+1xt+2... by a new variant of algorithmic complexity of m given x, plus the complexity of the randomness deficiency of x. The new complexity is monotone in its condition in the sense that this complexity can only decrease if the condition is prolonged. We also briefly discuss potential generalizations to Bayesian model classes and to classification problems.

U2 - 10.1007/11564089_32

DO - 10.1007/11564089_32

M3 - Conference contribution with ISSN or ISBN

VL - 3734

T3 - Lecture Notes in Computer Science

SP - 414

EP - 428

BT - Proceedings of the 16th International Conference Algorithmic Learning Theory 2005

CY - Berlin Heidelberg

ER -