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Pareto Upper Confidence Bounds algorithms: An empirical study

contributor authorDrugan, Madalina M
contributor authorNowe, Ann
contributor authorManderick, Bernard
date accessioned2020-03-12T22:27:44Z
date available2020-03-12T22:27:44Z
date issued2014
identifier other7010620.pdf
identifier urihttps://libsearch.um.ac.ir:443/fum/handle/fum/1083825?locale-attribute=en&show=full
formatgeneral
languageEnglish
publisherIEEE
titlePareto Upper Confidence Bounds algorithms: An empirical study
typeConference Paper
contenttypeMetadata Only
identifier padid8221052
subject keywordsdata analysis
subject keywordsn fuzzy neural nets
subject keywordsn learning (artificial intelligence)
subject keywordsn time series
subject keywordsn Dow Jones Indices data
subject keywordsn FNN
subject keywordsn Lorenz data
subject keywordsn Mackey-Glass data
subject keywordsn Sunspot data
subject keywordsn complex nonlinear time-series prediction
subject keywordsn fuzzy neural network ensemble
subject keywordsn improved boosting scheme
subject keywordsn modified AdaBoost regression and threshold algorithm
subject keywordsn modified AdaBoostRT algorithm
subject keywordsn time-series data
subject keywordsn Boosting
subject keywordsn Classification algorithms
subject keywordsn Equations
subject keywordsn Fuzzy neural network
identifier doi10.1109/IJCNN.2014.6889431
journal titledaptive Dynamic Programming and Reinforcement Learning (ADPRL), 2014 IEEE Symposium on
filesize191731
citations0


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