Bayesian approaches to supervised learning use priors on the classifier parameters. However, few priors aim at achieving "sparse" classifiers, where irrelevant/redundant parameters are automatically set to zero. Two well-known ways of obtaining sparse classifiers are: use a zero-mean Laplacian prior on the parameters, and the "support vector machine" (SVM). Whether one uses a Laplacian prior or an SVM, one still needs to specify/estimate the parameters that control the degree of sparseness of the resulting classifiers. We propose a Bayesian approach to learning sparse classifiers which does not involve any parameters controlling the degree of sparseness. This is achieved by a hierarchical-Bayes interpretation of the Laplacian prior, followed by the adoption of a Jeffreys' non-informative hyper-prior Implementation is carried out by an EM algorithm. Experimental evaluation of the proposed method shows that it performs competitively with (often better than) the best classification techniques available.