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- W2897038973 abstract "We solve a problem of hyperparameter optimization for classification with two objectives: find solutions with low generalization error and simultaneously sparse. We designed a theoretical and practical framework for combining multiple measures for hyperparameter optimization. The theoretical framework is based on selecting measures of knowledge and uncertainty fulfilling axiomatic systems for classification problems. The practical framework is based on solving multi-objective optimization problem. We validated our model on real world data sets with support vector machines (SVM) and we achieved substantially smaller number of support vectors with similar generalization performance compared to cross validation with misclassification error." @default.
- W2897038973 created "2018-10-26" @default.
- W2897038973 creator A5036747819 @default.
- W2897038973 date "2018-07-01" @default.
- W2897038973 modified "2023-10-14" @default.
- W2897038973 title "Knowledge-Uncertainty Axiomatized Framework with Support Vector Machines for Sparse Hyperparameter Optimization" @default.
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- W2897038973 doi "https://doi.org/10.1109/ijcnn.2018.8489144" @default.
- W2897038973 hasPublicationYear "2018" @default.
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