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https://github.com/svaniksharma/relevancevectormachine.jl

An implementation of a relevance vector machine, a sparse Bayesian version of a SVM
https://github.com/svaniksharma/relevancevectormachine.jl

classification-models julia julia-package machine-learning machine-learning-algorithms machine-learning-models regression-models relevance-vector-machine

Last synced: 18 days ago
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An implementation of a relevance vector machine, a sparse Bayesian version of a SVM

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# RelevanceVectorMachine.jl

![build](https://github.com/svaniksharma/RelevanceVectorMachine.jl/actions/workflows/CI.yml/badge.svg)
![documentation](https://github.com/svaniksharma/RelevanceVectorMachine.jl/actions/workflows/Documentation.yml/badge.svg)
![Julia](https://img.shields.io/badge/-Julia-9558B2?style=for-the-badge&logo=julia&logoColor=white)

A [relevance vector machine](http://proceedings.mlr.press/r4/tipping03a/tipping03a.pdf) implementation written in Julia.

## Installation

Use Julia's [Pkg](https://docs.julialang.org/en/v1/stdlib/Pkg/) module to install it:

```julia
import Pkg; Pkg.add("RelevanceVectorMachine.jl")
```

## Quick Start

```julia
import RelevanceVectorMachine
using RDatasets
using StatsModels

boston_data = RDatasets.dataset("MASS", "Boston")

rvm = RelevanceVectorMachine.rvm(@formula(MedV ~ Rm), boston_data)

# Predict on new data
preds = RelevanceVectorMachine.predict(rvm, new_boston_data)
```

See the [examples](https://github.com/svaniksharma/RelevanceVectorMachine.jl/tree/master/examples) folder for more.

## Contributing

Pull requests are welcome. For major changes, please open an issue first
to discuss what you would like to change.

Please make sure to update tests as appropriate.

## License

[MIT](https://choosealicense.com/licenses/mit/)