Leaving the Span


We discuss a simple sparse linear problem that is hard to learn with any algorithm that uses a linear combination of the training instances as its weight vector. The hardness holds even if we allow the learner to embed the instances into any higher dimensional feature space (and use a kernel function to define the dot product between the embedded instances… (More)
DOI: 10.1007/11503415_25


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