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ShareBoost
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The shareBoost algorithm learns a multiclass predictor from a subset of shared features of the samples with forward greedy selection approach.

See :cite:`shalev2011shareboost` for a detailed introduction.

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Example
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Imagine we have files with training and test data. We create CDenseFeatures (here 64 bit floats aka RealFeatures) and :sgclass:`CMulticlassLabels` as

.. sgexample:: shareboost.sg:create_features

We create an instance of the :sgclass:`CShareBoost` classifier by setting the number of features expected to be used for learning.

.. sgexample:: shareboost.sg:create_instance

Then we train and apply it to test data, which gives :sgclass:`CMulticlassLabels`.

.. sgexample:: shareboost.sg:train_and_apply

We can evaluate test performance via e.g. :sgclass:`CMulticlassAccuracy`.

.. sgexample:: shareboost.sg:evaluate_accuracy

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References
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.. bibliography:: ../../references.bib
    :filter: docname in docnames