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Optimal Estimation
"... The first quest for optimal estimation by Fisher, [2], Cramer, Rao and others, [1], dates back to over half a century and has changed remarkably little. The covariance of the estimated parameters was taken as the quality measure of estimators, for which the main result, the CramerRao inequality, se ..."
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The first quest for optimal estimation by Fisher, [2], Cramer, Rao and others, [1], dates back to over half a century and has changed remarkably little. The covariance of the estimated parameters was taken as the quality measure of estimators, for which the main result, the CramerRao inequality
Estimating the Support of a HighDimensional Distribution
, 1999
"... Suppose you are given some dataset drawn from an underlying probability distribution P and you want to estimate a "simple" subset S of input space such that the probability that a test point drawn from P lies outside of S is bounded by some a priori specified between 0 and 1. We propo ..."
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Cited by 766 (29 self)
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Suppose you are given some dataset drawn from an underlying probability distribution P and you want to estimate a "simple" subset S of input space such that the probability that a test point drawn from P lies outside of S is bounded by some a priori specified between 0 and 1. We
Pegasos: Primal Estimated subgradient solver for SVM
"... We describe and analyze a simple and effective stochastic subgradient descent algorithm for solving the optimization problem cast by Support Vector Machines (SVM). We prove that the number of iterations required to obtain a solution of accuracy ɛ is Õ(1/ɛ), where each iteration operates on a singl ..."
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Cited by 531 (21 self)
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We describe and analyze a simple and effective stochastic subgradient descent algorithm for solving the optimization problem cast by Support Vector Machines (SVM). We prove that the number of iterations required to obtain a solution of accuracy ɛ is Õ(1/ɛ), where each iteration operates on a