math/classias - The NetBSD Packages Collection

Collection of machine-learning algorithms for classification

Classias is a collection of machine-learning algorithms for
classification. Currently, it supports the following formalizations:
    L1/L2-regularized logistic regression (aka. Maximum Entropy)
    L1/L2-regularized L1-loss linear-kernel Support Vector Machine (SVM)
    Averaged perceptron
It implements several algorithms for training classifiers:
    Averaged perceptron
    Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) [Nocedal80]
    Orthant-Wise Limited-memory Quasi-Newton (OWL-QN) [Andrew07]
    Primal Estimated sub-GrAdient SOlver (Pegasos) [Shalev-Shwartz07]
    Truncated Gradient [Langford09], also known as FOrward LOoking
    Subgradient (FOLOS) [Duchi09] specialized for L1 regularization

Build dependencies

pkgtools/cwrappers devel/automake devel/autoconf

Runtime dependencies

math/liblbfgs

Available binary packages

i386:classias-1.1.0.20160722.tgz(NetBSD 8.0)
i386:classias-1.1.0.20160722.tgz(NetBSD 8.0)
i386:classias-1.1.0.20160722.tgz(NetBSD 8.0)
i386:classias-1.1.0.20160722.tgz(NetBSD 9.0)
i386:classias-1.1.0.20160722.tgz(NetBSD 9.0)
i386:classias-1.1.0.20160722.tgz(NetBSD 9.0)
x86_64:classias-1.1.0.20160722.tgz(NetBSD 9.0)
x86_64:classias-1.1.0.20160722.tgz(NetBSD 9.0)
x86_64:classias-1.1.0.20160722.tgz(NetBSD 9.0)
x86_64:classias-1.1.0.20160722.tgz(NetBSD 9.0)
x86_64:classias-1.1.0.20160722.tgz(NetBSD 8.0)
x86_64:classias-1.1.0.20160722.tgz(NetBSD 8.0)
x86_64:classias-1.1.0.20160722.tgz(NetBSD 8.0)
x86_64:classias-1.1.0.20160722.tgz(NetBSD 9.0)
x86_64:classias-1.1.0.20160722.tgz(NetBSD 9.0)
x86_64:classias-1.1.0.20160722.tgz(NetBSD 9.0)
x86_64:classias-1.1.0.20160722.tgz(NetBSD 9.0)

Binary packages can be installed with pkgin or pkg_add(1). The NetBSD packages collection is also designed to permit easy installation from source.

Available build options

(none)

Known vulnerabilities

The pkg_admin audit command locates any installed package which has been mentioned in security advisories as having vulnerabilities.

Please note the vulnerabilities database might not be fully accurate, and not every bug is exploitable with every configuration.


Problem reports, updates or suggestions for this package should be reported with send-pr.


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