Výsledky vyhledávání - "Nakata, Michinori"

Akademický článek

An Overview of the getRNIA System for Non-deterministic Data

Autor: Wu, Mao, Nakata, Michinori, Sakai, Hiroshi

Publikováno v: In Procedia Computer Science 2013 22:615-622

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NIS-Apriori Algorithm with a Target Descriptor for Handling Rules Supported by Minor Instances

Autor: Sakai, Hiroshi, Shen, Kao-Yi, Nakata, Michinori

Publikováno v: Lecture Notes in Computer Science. 11471:247-259

For each implication τ: Condition_part⇒ Decision_part defined in table data sets, we see τ is a rule if τ satisfies appropriate constraints, i.e., support(τ)≥α and accuracy(τ)≥β for two threshold values α and β (0
The seventh Inte

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=jairo_______::e091cc94bc7bbcc0930b62d9e10e3208
https://kyutech.repo.nii.ac.jp/records/7462

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On the Behavior of Fuzzy Grey Cognitive Maps

Autor: Concepción, Leonardo, Nápoles, Gonzalo, Bello, Rafael, Vanhoof, Koen, Miao, Duoqian, Falcon, Rafael, Nakata, Michinori, Rosete, Alejandro, Ciucci, Davide

Publikováno v: Rough Sets, 462-476
STARTPAGE=462;ENDPAGE=476;TITLE=Rough Sets

Fuzzy Cognitive Maps (FCMs) are recurrent neural networks made up of well-defined neurons and causal relations. Fuzzy Grey Cognitive Maps (FGCMs) are an extension of FCMs, intended to surpass the intrinsic uncertainties modeling real-world problems b

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=narcis______::2d97b135695e1689b4c0ce286fc169d0
https://research.tilburguniversity.edu/en/publications/35ae2a34-2c68-40dd-b27f-0ef5ca896c64

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Feature and Label Association Based on Granulation Entropy for Deep Neural Networks

Autor: Bello, Marilyn, Nápoles, Gonzalo, Sánchez, Ricardo, Vanhoof, Koen, Bello, Rafael, Miao, Duoqian, Falcon, Rafael, Nakata, Michinori, Rosete, Alejandro, Ciucci, Davide

Publikováno v: Rough Sets, 225-235
STARTPAGE=225;ENDPAGE=235;TITLE=Rough Sets

Pooling layers help reduce redundancy and the number of parameters before building a multilayered neural network that performs the remaining processing operations. Usually, pooling operators in deep learning models use an explicit topological organiz

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=narcis______::220edf1402450636e7e617d3654fb7d3
https://research.tilburguniversity.edu/en/publications/29bbb52a-d8b1-4444-aba9-502bb7f1af57

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