# Working One-class SVM in Julia?

**URL:** <https://discourse.julialang.org/t/working-one-class-svm-in-julia/43747>\
**Category:** General Usage\
**Tags:** question\
**Created:** [July 27, 2020, 8:39am UTC](https://discourse.julialang.org/t/working-one-class-svm-in-julia/43747 "2020-07-27T08:39:11Z")\
**Posts on this page:** 1\
**Showing post:** 2

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**Author:** ![baggepinnen](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/baggepinnen/32/693_2.png) [@baggepinnen](https://discourse.julialang.org/u/baggepinnen)\
**Post date:** [July 27, 2020, 9:03am UTC](https://discourse.julialang.org/t/working-one-class-svm-in-julia/43747/2 "2020-07-27T09:03:17Z")

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> [@\[ANN\] KernelMachines.jl](https://discourse.julialang.org/t/ann-kernelmachines-jl/43043):
>
> We are happy to announce the release of [KernelMachines.jl](https://gitlab.com/VeosDigital/KernelMachines.jl). Framework Kernel machines are a special case of a more general framework, parametric machines—see [this article](https://arxiv.org/abs/2007.02777) for technical details on the framework. The key idea of parametric machines in general (and kernel machines in particular) is to build large spaces of “neural network-like” architectures and ensure that those spaces have good geometrical properties: completeness and, for a regularized problem on a finite training dataset, com…

perhaps?

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