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An improved through line de-embedding method with even-odd mode measurement

contributor authorOupeng Li
contributor authorWei Cheng
contributor authorLei Wang
contributor authorHaiyan Lu
contributor authorRuimin Xu
date accessioned2020-03-12T21:17:52Z
date available2020-03-12T21:17:52Z
date issued2014
identifier other6933256.pdf
identifier urihttps://libsearch.um.ac.ir:443/fum/handle/fum/1043926?show=full
formatgeneral
languageEnglish
publisherIEEE
titleAn improved through line de-embedding method with even-odd mode measurement
typeConference Paper
contenttypeMetadata Only
identifier padid8172091
subject keywordsartificial immune systems
subject keywordscomputational linguistics
subject keywordslearning (artificial intelligence)
subject keywordsmulti-agent systems
subject keywordsnatural language processing
subject keywordsword processing
subject keywordsMWAALM
subject keywordsPenn Chinese Treebank 5.1
subject keywordsartificial immune system
subject keywordsartificial intelligence
subject keywordscomputational linguistics
subject keywordscontinuous learning
subject keywordsdependency syntax
subject keywordsdependent properties
subject keywordshead properties
subject keywordslymphocyte-style word representation
subject keywordsmultiword-agent autonomous learning model
subject keywordsrepresentation word context
subject keywordsCloning
subject keywordsContext
subject keywordsFeature extraction
subject keywordsHaf
identifier doi10.1109/ICInfA.2014.6932783
journal titleadio-Frequency Integration Technology (RFIT), 2014 IEEE International Symposium on
filesize244047
citations0


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