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Zhou et al., 2019 - Google Patents

A model-agnostic approach for explaining the predictions on clustered data

Zhou et al., 2019

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Document ID
16066768374256166976
Author
Zhou Z
Sun M
Chen J
Publication year
Publication venue
2019 IEEE international conference on data mining (ICDM)

External Links

Snippet

Machine learning models especially deep neural network models have shown great potential in making decisions when analyzing clustered or longitudinal data. However, lack of model transparency is a major concern in risk sensitive domains such as social science …
Continue reading at zihan-zhou.github.io (PDF) (other versions)

Classifications

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    • G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
    • G06F17/30587—Details of specialised database models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
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    • G06F17/30867—Retrieval from the Internet, e.g. browsers by querying, e.g. search engines or meta-search engines, crawling techniques, push systems with filtering and personalisation
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