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- W2798539803 abstract "Training of multi-class or multi-label classification machines are embarrassingly parallelizable via the one-vs.-rest approach. However, training of all-in-one multi-class learning machines such as multinomial logistic regression or all-in-one multi-class SVMs (MC-SVMs) is not parallelizable out of the box. In my talk, I present optimization strategies to distribute the training of some all-in-one multi-class SVMs over the classes, which makes them appealing for the use in extreme classification." @default.
- W2798539803 created "2018-05-07" @default.
- W2798539803 creator A5091841504 @default.
- W2798539803 date "2018-01-01" @default.
- W2798539803 modified "2023-09-26" @default.
- W2798539803 title "Distributed Optimization of All-in-one SVMs for Extreme Classfication" @default.
- W2798539803 doi "https://doi.org/10.1145/3184558.3193137" @default.
- W2798539803 hasPublicationYear "2018" @default.
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