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Learning to generate classifiers

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 نشر من قبل Nicholas Guttenberg
 تاريخ النشر 2018
  مجال البحث الهندسة المعلوماتية
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We train a network to generate mappings between training sets and classification policies (a classifier generator) by conditioning on the entire training set via an attentional mechanism. The network is directly optimized for test set performance on an training set of related tasks, which is then transferred to unseen test tasks. We use this to optimize for performance in the low-data and unsupervised learning regimes, and obtain significantly better performance in the 10-50 datapoint regime than support vector classifiers, random forests, XGBoost, and k-nearest neighbors on a range of small datasets.



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