Source: libocas Section: libs Priority: optional Maintainer: Christian Kastner Build-Depends: debhelper-compat (= 12), Rules-Requires-Root: no Standards-Version: 4.4.1 Homepage: http://cmp.felk.cvut.cz/~xfrancv/ocas/html/ Vcs-Git: https://salsa.debian.org/science-team/libocas.git Vcs-Browser: https://salsa.debian.org/science-team/libocas Package: libocas-dev Section: libdevel Architecture: any Multi-Arch: same Depends: ${misc:Depends}, libocas0 (= ${binary:Version}), Description: Development libraries and header files for LIBOCAS This library implements Optimized Cutting Plane Algorithm (OCAS) for training linear Support Vector Machine (SVM) classifiers from large-scale data. The computational effort of OCAS scales linearly with the number of training examples. It is one of the fastest SVM solvers around for solving linear and multiclass L2 regularized SVMs. . This package contains the header files and static libraries. Package: libocas0 Architecture: any Multi-Arch: same Pre-Depends: ${misc:Pre-Depends}, Depends: ${shlibs:Depends}, ${misc:Depends}, Suggests: libocas-tools (= ${binary:Version}), Description: OCAS solver for training linear SVM classifiers This library implements Optimized Cutting Plane Algorithm (OCAS) for training linear Support Vector Machine (SVM) classifiers from large-scale data. The computational effort of OCAS scales linearly with the number of training examples. It is one of the fastest SVM solvers around for solving linear and multiclass L2 regularized SVMs. . This package contains the shared libraries. Package: libocas-tools Section: science Architecture: any Multi-Arch: foreign Depends: ${shlibs:Depends}, ${misc:Depends}, libocas0 (= ${binary:Version}), Description: Standalone applications implementing the OCAS solver This library implements Optimized Cutting Plane Algorithm (OCAS) for training linear Support Vector Machine (SVM) classifiers from large-scale data. The computational effort of OCAS scales linearly with the number of training examples. It is one of the fastest SVM solvers around for solving linear and multiclass L2 regularized SVMs. . This package contains the standalone applications.