Support Vector Machines (SVM) in pure Julia
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Project Status: Suspended - Initial development has started, but there has not yet been a stable, usable release; work has been stopped for the time being but the author(s) intend on resuming work. License


A Julia package for research and application of linear- and non-linear Support Vector Machines (SVM).

This package aims to make use of Julia's strength in order to provide an easily extensible, highly modular, and at the same time computationally efficient framework for SVMs.

Why Julia?

Julia's high-level language design (which should feel familiar to the scientific community), combined with it's high-performance, may have the potential to bridge the two-language problem, that is arguably associated with research and application of machine learning algorithms.

It is our hope, that frameworks such as this enable a broader range of scientists to contribute effective algorithms with fair comparisons, and without having to be fluent in low-level languages such as C

  • TODO some objective benchmarks

Just for research?

While research is an important goal of this framework, it is of equal importance that all the included algorithms are 1.) well documented, and thanks to Julia 2.) efficient. Additionally, because of the framework's modular and transparent interface (e.g. callback functions), it should be simple to use this library as basis for course exercises.

Bottom Line: The use of this framework for educational and/or commercial reasons is encouraged under the MIT "Expat" license.


This is still a work in progress. Installation not yet advised.


Here is a quick "Hello World" example of using a linear SVM on the example of the Iris dataset.

# Adapted example from SVM.jl by John Myles White
using KSVM
using RDatasets
iris = dataset("datasets", "iris")
X = convert(Array, iris[1:100, 1:2])'  # The observations have to be in the columns
y = iris[1:100, :Species]
train = bitrand(size(X,2))             # Split training and testset
model = svm(X[:, train], y[train])     # Fit the linear SVM
acc = accuracy(model, X[:, ~train], y[~train])

Iris Data SVM

Note: The preview plot on the bottom is only provided if the dataset is small and lies in two dimensions

Linear Support Vector Machines

svm(X, y⃗; nargs...) → SVM

The function svm fits a Support Vector Machine (SVM) to the given training data X and y⃗. Depending on the specified parameters in nargs, the SVM can be trained to perform various forms of regression or classification tasks. There are a number of parameters that can be configured to the individual needs, such as the type of lossfunction, the type of regularization, or the algorithm used for training the SVM in the primal/dual.


  • X : The input matrix of the training set, with the observations as columns. It can be either a dense or sparse matrix of floatingpoint numbers. Note that it is generally advised to center and scale your data before training a SVM.

  • y⃗ : The target vector of the training set. It has to be the same length as X has columns. In the case of classification, it can be either a vector with elements yᵢ ∈ {-1.0, 1.0} for two class prediction, or a vector with elements yᵢ ∈ N for multi-class prediction.

  • solver : The concrete algorithm to train the SVM. Depending on the algorithm this will either solve the linear SVM in the primal or dual formulation. In contrast to other frameworks, the algorithm does not determine if the solution of the primal or dual problem is returned (see parameter dual).

  • kernel : The kernel that should be used. The ScalarProductKernel denotes the special case of a linear SVM and allows for additional functionality and solvers that are specialized for the linear case.

  • loss : The utilized loss function. The typical loss functions for SVMs are HingeLoss() (L1-SVM), L2HingeLoss() (L2-SVM), SmoothedL1HingeLoss(h), or ModifiedHuberLoss() for classification, and EpsilonInsLoss(e), or L2EpsilonInsLoss(e) for support vector regression. Note that in general each solvers is only able to deal with a subset of those loss functions and some combinations might not be supported. For example the implementation of DualCD does only support HingeLoss and L2HingeLoss.

  • regtype : The type of regularization that should be used. In general this can either be L1Penalty or L2Penalty. Note that not all solver support L1Penalty.

  • bias : The scaling factor of the bias. If set to 0, no intercept will be fitted.

  • C : The penalty parameter. It plays a role similar to λ⁻¹ in Empirical Risk Minimization. Conceptually, it denotes the trade off between model complexity and the training error. Larger C will increase the penalization of training errors and thus lead to behavior similar to a hard margin classifier.

  • dual : Boolean that specifies if the solution to the dual problem should be returned instead of the solution to the primal (default = false).

    In contrast to other frameworks, the dual parameter does not influence whether the dual or the primal problem is solved (this is instead specified implicitly by the solver parameter), but the result the user is interested in. It is generally recommended to leave dual = false, unless the user is either explicitly interested in the support vectors, or the dataset is sparse and high-dimensional.

    Note: Not all solver are able to natively provide both solutions. If the algorithm is unable to provide the desired solution type then the solution will be converted accordingly, if feasible.

  • iterations : Specifies the maximum number of iterations before the solver exits with the current (and probably suboptimal) solution.

  • ftol : Specifies the tolerance for the change of the objective function value.

  • xtol : Specifies the tolerance for the change of the solution.

  • gtol : Specifies the tolerance for the change of the gradient norm.

  • callback : The optional callback function with signature f(i, w, v, G). If a callback is specified, then it will be called every iteration with the following four parameters in order:

    param description
    i the current iteration number.
    w the current coefficients (i.e. α if dual=true, or θ if dual=false).
    v the current objective value.
    G the current gradient.

    Note: The callback function can be used for early stopping by returning the symbol :Exit.

  • show_trace : Instead of (or additional to) the callback function, the user can enable the output of the training information to STDOUT.

  • nargs... : Additional named arguments that are passed unchecked to the specified solver. This functionality can be used to pass around special arguments that the library does not natively implement.

Non-linear Support Vector Machines

coming soon


This code is free to use under the terms of the MIT license.


This package makes heavy use of the following packages in order to provide it's main functionality. To see at full list of utilized packages, please take a look at the REQUIRE file.

Early inspiration by Regression.jl


  • Chapelle, Olivier. "Training a support vector machine in the primal." Neural Computation 19.5 (2007): 1155-1178. link

  • Hsieh, Cho-Jui, et al. "A dual coordinate descent method for large-scale linear SVM." Proceedings of the 25th International Conference on Machine learning (ICML-08). ACM, 2008. link

  • Platt, John. "Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods." Advances in large margin classifiers 10.3 (1999): 61-74. link

  • Platt, John. "Fast training of support vector machines using sequential minimal optimization." Advances in kernel methods—support vector learning 3 (1999). link

  • Shalev-shwartz, Shai, et al. "Pegasos: Primal Estimated sub-GrAdient SOlver for SVM." Proceedings of the 24th International Conference on Machine Learning (ICML-07). 2007. link