2013
DOI: 10.1016/j.eswa.2013.07.041
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Linear Bayes policy for learning in contextual-bandits

Abstract: a b s t r a c tMachine and Statistical Learning techniques are used in almost all online advertisement systems. The problem of discovering which content is more demanded (e.g. receive more clicks) can be modeled as a multi-armed bandit problem. Contextual bandits (i.e., bandits with covariates, side information or associative reinforcement learning) associate, to each specific content, several features that define the ''context'' in which it appears (e.g. user, web page, time, region). This problem can be stud… Show more

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Cited by 5 publications
(2 citation statements)
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References 27 publications
(25 reference statements)
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“…The KICA is used to eliminate noise and extract sensitive fault signals, and the BPNN is used for fault pattern recognition. The details behind the KICA and GA-BPNN theories refer to [5] and [8]. The workflow of the proposed new method for gear faults is shown in Fig.…”
Section: The Proposed Fault Diagnosis Methodsmentioning
confidence: 99%
“…The KICA is used to eliminate noise and extract sensitive fault signals, and the BPNN is used for fault pattern recognition. The details behind the KICA and GA-BPNN theories refer to [5] and [8]. The workflow of the proposed new method for gear faults is shown in Fig.…”
Section: The Proposed Fault Diagnosis Methodsmentioning
confidence: 99%
“…The PCA is used to reduce the dimensionality of the fault feature vector, and the SOM is applied to the fault pattern recognition. The details behind the PCA and SOM theories refer to [5] and [18]. The workflow diagram block of the proposed diagnosis method for the rolling bearing is shown in Fig.…”
Section: The Proposed Fault Diagnosis Methodsmentioning
confidence: 99%