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- W2377214897 abstract "To address the problem of SVDD(support vector data description) processing larget samples dataset with huge time complexity,a novel RGInc-SVDD(random greed incremental SVDD) algorithm was proposed.Firstly,using the SL(sampling lemma) to divide the training samples dataset into several small samples subsets;secondly,create an Inc-SVDDi model with one of samples subsets.Then,the rule of interactive random greed was applied to grow the Inc-SVDDi until the SVDD being created with whole training samples information.The RGInc-SVDD algorithm makes the time complexity significantly decrease from O(N3) to O(N2r/Gn2/k),where respectively denote the number of training samples and the number of random greed in each interactive step." @default.
- W2377214897 created "2016-06-24" @default.
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- W2377214897 date "2010-01-01" @default.
- W2377214897 modified "2023-09-27" @default.
- W2377214897 title "Random greed incremental SVDD algorithm and its application" @default.
- W2377214897 hasPublicationYear "2010" @default.
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