948 resultados para Williamson, Robert McAlpine, 1804-1859.
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On spine: Texas' three-legged Willie.
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"Ein Blatt von Robert Schumanns Grab gepflückt. Erhalten zu Bonn / 11.3.[18]59 / von Kapellmeister Peters."
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The paper "the importance of convexity in learning with squared loss" gave a lower bound on the sample complexity of learning with quadratic loss using a nonconvex function class. The proof contains an error. We show that the lower bound is true under a stronger condition that holds for many cases of interest.
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Convex potential minimisation is the de facto approach to binary classification. However, Long and Servedio [2008] proved that under symmetric label noise (SLN), minimisation of any convex potential over a linear function class can result in classification performance equivalent to random guessing. This ostensibly shows that convex losses are not SLN-robust. In this paper, we propose a convex, classification-calibrated loss and prove that it is SLN-robust. The loss avoids the Long and Servedio [2008] result by virtue of being negatively unbounded. The loss is a modification of the hinge loss, where one does not clamp at zero; hence, we call it the unhinged loss. We show that the optimal unhinged solution is equivalent to that of a strongly regularised SVM, and is the limiting solution for any convex potential; this implies that strong l2 regularisation makes most standard learners SLN-robust. Experiments confirm the unhinged loss’ SLN-robustness.
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Engraved title vignette.
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Mode of access: Internet.
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Mode of access: Internet.
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"Life of ... Francis Bacon," by William Rawley : v. 1, p. 1-18.
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En port.: Ouvrage qui fait suite à celui de M. Toullier, mais dans lequel on a adopté la forme plus commode du commentaire.
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Mode of access: Internet.
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Mode of access: Internet.
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First edition, London, 1844; an American edition was published New York, Wiley and Putnam, 1845.
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Mode of access: Internet.
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"Errata": tipped in before p. 3.
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Mode of access: Internet.