Result: Well-calibrated predictions from on-line compression models
Title:
Well-calibrated predictions from on-line compression models
Authors:
Source:
Algorithmic learning theoryTheoretical computer science. 364(1):10-26
Publisher Information:
Amsterdam: Elsevier, 2006.
Publication Year:
2006
Physical Description:
print, 32 ref
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Informatique théorique, Theoretical computing, Automates. Machines abstraites. Machines de turing, Automata. Abstract machines. Turing machines, Algorithmique. Calculabilité. Arithmétique ordinateur, Algorithmics. Computability. Computer arithmetics, Distribution, Distribución, Informatique théorique, Computer theory, Informática teórica, Modèle Markov, Markov model, Modelo Markov, Modèle linéaire, Linear model, Modelo lineal, Prédiction, Prediction, Predicción, Modèle Gauss, Modèle hypergraphique, Hypergraphical model, Modèle échangeabilité, Exchangeability model, Modélisation compression en ligne, On-line compression modelling, Transducteur confiance, Confidence transducer, Gauss linear model, Gaussian model, Transductive confidence machine
Document Type:
Conference
Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Computer Learning Research Centre, Department of Computer Silence, Royal Holloway, University of London, Egham, Surrey TW20 0EX, United Kingdom
ISSN:
0304-3975
Rights:
Copyright 2006 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.18267548
Database:
PASCAL Archive
Further Information
It has been shown recently that transductive confidence machine (TCM) is automatically well-calibrated when used in the on-line mode and provided that the data sequence is generated by an exchangeable distribution. In this paper we strengthen this result by relaxing the assumption of exchangeability of the data-generating distribution to the much weaker assumption that the data agrees with a given on-line compression model.