AWhat changed
Integrated coordinate-descent and trust-region solvers for linear support-vector and logistic models with modular C/C++ training/prediction interfaces, targeting large sparse classification data.
Scientific scope
Integrated coordinate-descent and trust-region solvers for linear support-vector and logistic models with modular C/C++ training/prediction interfaces, targeting large sparse classification data. Reuses separately described optimization methods and LIBSVM-style interfaces; software integration is not invention of logistic regression or SVMs. Linear-model speed advantages depend on data geometry and do not imply superiority over nonlinear kernels on every task.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Reuses separately described optimization methods and LIBSVM-style interfaces; software integration is not invention of logistic regression or SVMs. Linear-model speed advantages depend on data geometry and do not imply superiority over nonlinear kernels on every task.
Contributors
- Source-listed authors and technical contributors · SCIENCE: Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.
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