Trading Accuracy for Sparsity
نویسندگان
چکیده
We study the problem of minimizing the expected loss of a linear predictor while constraining the sparsity of the predictor, i.e. bounding the number of features used by the predictor. While this problem is generally NP-hard, we describe several approximation algorithms. We analyze the performance of our algorithms, focusing on the characterization of the trade-off between accuracy and sparsity of the learned predictor in different scenarios.
منابع مشابه
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تاریخ انتشار 2009