On b-bit min-wise hashing for large-scale regression and classification with sparse data
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Abstract
Large-scale regression problems where both the number of variables,
We also show that ordinary least squares or ridge regression applied to the reduced data can in fact allow us fit more flexible models. We obtain non-asymptotic prediction error bounds for interaction models and for models where an unknown row normalisation must be applied in order for the signal to be linear in the predictors.
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1533-7928