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3Prediction with Expert Advice by Following the Perturbed Leader for General Weights
M. Hutter, и J. Poland. Proc. 15th International Conf. on Algorithmic Learning Theory (ALT'04), том 3244 из LNAI, стр. 279--293. Padova, Springer, Berlin, (2004)
17 лет назад , @idsia
advice,
high,
experts,
bounds,
adaptive,
hierarchy,
general,
the,
with
learning,
of,
expert,
expected,
alphabet,
sequential,
rate,
leader,
probability,
perturbed,
loss,
follow,
online,
juergen,
prediction,
and,
weights,
advice,high,experts,bounds,adaptive,hierarchy,general,the,withlearning,of,expert,expected,alphabet,sequential,rate,leader,probability,perturbed,loss,follow,online,juergen,prediction,and,weights,
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2Universal Sequential Decisions in Unknown Environments
M. Hutter. Proc. 5th European Workshop on Reinforcement Learning (EWRL-5), 27, стр. 25--26. Onderwijsinsituut CKI, Utrecht Univ., (2001)
17 лет назад , @idsia
solomonoff,
decision,
computational,
reinforcement,
rational,
theory,
probability,
kolmogorov,
induction,
intelligence,
algorithmic,
learning,
juergen,
artificial,
complexity,
universal
sequential,
agents,
solomonoff,decision,computational,reinforcement,rational,theory,probability,kolmogorov,induction,intelligence,algorithmic,learning,juergen,artificial,complexity,universalsequential,agents,
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2Master Algorithms for Active Experts Problems based on Increasing Loss Values
J. Poland, и M. Hutter. Annual Machine Learning Conference of Belgium and the Netherlands (Benelearn-2005), Enschede, (2005)
17 лет назад , @idsia
game,
advice,
optimality,
universal,
bandits,
observation,
with
learning,
expert,
environments,
juergen,
partial,
prediction,
responsive,
asymptotic,
game,advice,optimality,universal,bandits,observation,withlearning,expert,environments,juergen,partial,prediction,responsive,asymptotic,
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3Defensive Universal Learning with Experts
J. Poland, и M. Hutter. Proc. 16th International Conf. on Algorithmic Learning Theory (ALT'05), том 3734 из LNAI, стр. 356--370. Singapore, Springer, Berlin, (2005)
17 лет назад , @idsia
game,
advice,
optimality,
universal,
bandits,
observation,
with
learning,
expert,
environments,
juergen,
partial,
prediction,
responsive,
asymptotic,
game,advice,optimality,universal,bandits,observation,withlearning,expert,environments,juergen,partial,prediction,responsive,asymptotic,
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2Strong Asymptotic Assertions for Discrete MDL in Regression and Classification
J. Poland, и M. Hutter. Annual Machine Learning Conference of Belgium and the Netherlands (Benelearn-2005), Enschede, (2005)
17 лет назад , @idsia
classes,
minimum,
convergence,
model,
bayes,
sequence
regression,
machine,
learning,
description,
juergen,
mixture,
prediction,
classification,
discrete,
marginalization,
length,
classes,minimum,convergence,model,bayes,sequenceregression,machine,learning,description,juergen,mixture,prediction,classification,discrete,marginalization,length,
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3Adaptive Online Prediction by Following the Perturbed Leader
M. Hutter, и J. Poland. Journal of Machine Learning Research, (2005)
17 лет назад , @idsia
advice,
high,
experts,
bounds,
adaptive,
hierarchy,
general,
adversary,
the,
with
learning,
of,
expert,
expected,
alphabet,
sequential,
rate,
leader,
probability,
perturbed,
loss,
follow,
online,
juergen,
prediction,
and,
weights,
advice,high,experts,bounds,adaptive,hierarchy,general,adversary,the,withlearning,of,expert,expected,alphabet,sequential,rate,leader,probability,perturbed,loss,follow,online,juergen,prediction,and,weights,
копироватьудалитьдобавить публикацию в буфер
3Defensive Universal Learning with Experts
J. Poland, и M. Hutter. Proc. 16th International Conf. on Algorithmic Learning Theory (ALT'05), том 3734 из LNAI, стр. 356--370. Singapore, Springer, Berlin, (2005)
17 лет назад , @schaul
game,
advice,
optimality,
universal,
bandits,
observation,
with
learning,
expert,
environments,
juergen,
partial,
prediction,
responsive,
asymptotic,
game,advice,optimality,universal,bandits,observation,withlearning,expert,environments,juergen,partial,prediction,responsive,asymptotic,
копироватьудалитьдобавить публикацию в буфер
2Strong Asymptotic Assertions for Discrete MDL in Regression and Classification
J. Poland, и M. Hutter. Annual Machine Learning Conference of Belgium and the Netherlands (Benelearn-2005), Enschede, (2005)
17 лет назад , @schaul
classes,
minimum,
convergence,
model,
bayes,
sequence
regression,
machine,
learning,
description,
juergen,
mixture,
prediction,
classification,
discrete,
marginalization,
length,
classes,minimum,convergence,model,bayes,sequenceregression,machine,learning,description,juergen,mixture,prediction,classification,discrete,marginalization,length,
копироватьудалитьдобавить публикацию в буфер
2Master Algorithms for Active Experts Problems based on Increasing Loss Values
J. Poland, и M. Hutter. Annual Machine Learning Conference of Belgium and the Netherlands (Benelearn-2005), Enschede, (2005)
17 лет назад , @schaul
game,
advice,
optimality,
universal,
bandits,
observation,
with
learning,
expert,
environments,
juergen,
partial,
prediction,
responsive,
asymptotic,
game,advice,optimality,universal,bandits,observation,withlearning,expert,environments,juergen,partial,prediction,responsive,asymptotic,
копироватьудалитьдобавить публикацию в буфер
3Adaptive Online Prediction by Following the Perturbed Leader
M. Hutter, и J. Poland. Journal of Machine Learning Research, (2005)
17 лет назад , @schaul
advice,
high,
experts,
bounds,
adaptive,
hierarchy,
general,
adversary,
the,
with
learning,
of,
expert,
expected,
alphabet,
sequential,
rate,
leader,
probability,
perturbed,
loss,
follow,
online,
juergen,
prediction,
and,
weights,
advice,high,experts,bounds,adaptive,hierarchy,general,adversary,the,withlearning,of,expert,expected,alphabet,sequential,rate,leader,probability,perturbed,loss,follow,online,juergen,prediction,and,weights,
копироватьудалитьдобавить публикацию в буфер
2Universal Sequential Decisions in Unknown Environments
M. Hutter. Proc. 5th European Workshop on Reinforcement Learning (EWRL-5), 27, стр. 25--26. Onderwijsinsituut CKI, Utrecht Univ., (2001)
17 лет назад , @schaul
solomonoff,
decision,
computational,
reinforcement,
rational,
theory,
probability,
kolmogorov,
induction,
intelligence,
algorithmic,
learning,
juergen,
artificial,
complexity,
universal
sequential,
agents,
solomonoff,decision,computational,reinforcement,rational,theory,probability,kolmogorov,induction,intelligence,algorithmic,learning,juergen,artificial,complexity,universalsequential,agents,
копироватьудалитьдобавить публикацию в буфер
3Prediction with Expert Advice by Following the Perturbed Leader for General Weights
M. Hutter, и J. Poland. Proc. 15th International Conf. on Algorithmic Learning Theory (ALT'04), том 3244 из LNAI, стр. 279--293. Padova, Springer, Berlin, (2004)
17 лет назад , @schaul
advice,
high,
experts,
bounds,
adaptive,
hierarchy,
general,
the,
with
learning,
of,
expert,
expected,
alphabet,
sequential,
rate,
leader,
probability,
perturbed,
loss,
follow,
online,
juergen,
prediction,
and,
weights,
advice,high,experts,bounds,adaptive,hierarchy,general,the,withlearning,of,expert,expected,alphabet,sequential,rate,leader,probability,perturbed,loss,follow,online,juergen,prediction,and,weights,
копироватьудалитьдобавить публикацию в буфер

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публикации (спрятать)12
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3Prediction with Expert Advice by Following the Perturbed Leader for General Weights

2Universal Sequential Decisions in Unknown Environments

2Master Algorithms for Active Experts Problems based on Increasing Loss Values

3Defensive Universal Learning with Experts

2Strong Asymptotic Assertions for Discrete MDL in Regression and Classification

3Adaptive Online Prediction by Following the Perturbed Leader

3Defensive Universal Learning with Experts

2Strong Asymptotic Assertions for Discrete MDL in Regression and Classification

2Master Algorithms for Active Experts Problems based on Increasing Loss Values

3Adaptive Online Prediction by Following the Perturbed Leader

2Universal Sequential Decisions in Unknown Environments

3Prediction with Expert Advice by Following the Perturbed Leader for General Weights

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