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Abstract:
In this paper we introduce a new sparseness inducing prior
which does not involve any (hyper)parameters that need to be
adjusted or estimated. Although other applications are possible,
we focus here on supervised learning problems: regression and
classification. Experiments with several publicly available
benchmark data sets show that the proposed approach yields
state-of-the-art performance. In particular, our method
outperforms support vector machines and performs competitively
with the best alternative techniques, both in terms of error
rates and sparseness, although it involves no tuning or adjusting
of sparseness-controlling hyper-parameters.
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