منابع مشابه
Random Lasso.
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Many statistical machine learning algorithms (in regression or classification) minimize either an empirical loss function as in AdaBoost, or a penalized empirical loss as in SVM. A single regularization tuning parameter controls the trade-off between fidelity to the data and generalibility, or equivalently between bias and variance. When this tuning parameter changes, a regularization “path” of...
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The Lasso is an attractive regularisation method for high dimensional regression. It combines variable selection with an efficient computational procedure. However, the rate of convergence of the Lasso is slow for some sparse high dimensional data, where the number of predictor variables is growing fast with the number of observations. Moreover, many noise variables are selected if the estimato...
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ژورنال
عنوان ژورنال: The Annals of Applied Statistics
سال: 2011
ISSN: 1932-6157
DOI: 10.1214/10-aoas377