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大数据分析的无限深度神经网络方法.pdf

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计算机研究与发展 of and Journal Research 53(1):68—79,2016 Computer Development 大数据分析的无限深度神经网络方法 张 蕾 章 毅 (四川大学计算机学院机器智能实验室成都610065) (1eizhang@scu.edu.cn) Data InfiniteNeuralNetworks Big Analysisby Deep Leiand Yi Zhang Zhang (^,妃f^i订P,咒z已zzigP卵f已Ln60r口fory,CoZZPgPo,CoⅢ户“£PrSfiP超fP,Sif丘“n行L7”i口Prsi£了,C^P以gd“610065) Abstractneuralnetworks(DNNs)andtheir arewellknownintheacademic Deep learningalgorithms and asthemostsuccessfulmethodsfor data with communityindustry big analysis.Compared traditional methodsusedata—drivenandcanextractfeatures methods, deeplearning (knowledge) from methodshave in automaticallydata.[)eep1earning significantadvantagesanalyzing andvariedmodelandcrossfield data.At most used unstructured,unknown big widely present,the neuralnetworksin data arefeedforwardneural workwell deep big analysis networks(FNNs).They in thecorrelationfromstaticdataand fordata scenariosbasedon extracting suiting application Butlimiteditsintrinsic the neuralnetworksto classification. structurc, offccdforward by ability extracttime featuresisweak.Infiniteneuralnetworks,i.e.recurrentneuralnetworks sequence deep (RNNs)are Theiressentialcharacteristhatthestatesofthenetworks dynamicalsystemsessentially. withtimeand thetime Henceare
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