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http://users.cecs.anu.edu.au/~williams/papers/P132.pdf
1448 BernhardScholkop¨ fetal. Notethatifºapproaches0,theupperboundariesontheLagrangemul- tiplierstendtoin”nity,thatis,thesecondinequalityconstraintinequa-tion3 ...
https://www.researchgate.net/publication/220499623_Estimating_Support_of_a_High-Dimensional_Distribution
Estimating the Support of a High-Dimensional Distribution 1447 Since nonzero slack variables ξ i are penalized in the objective function, we can expect that if w and ρ solve this problem, then ...
https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/tr-99-87.pdf
Estimating the Support of a High-Dimensional Distribution Bernhard Sch¨olkopf?, John C. Platt z, John Shawe-Taylor y, Alex J. Smola x, Robert C. Williamson x, Microsoft Research Ltd, 1 Guildhall Street, Cambridge CB2 3NH, UKCited by: 4577
https://www.microsoft.com/en-us/research/publication/estimating-the-support-of-a-high-dimensional-distribution/
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 v between 0 and 1. We propose a […]Cited by: 4577
http://alex.smola.org/papers/2001/SchPlaShaSmoetal01.pdf
Estimating the Support of a High-Dimensional Distribution 1445 of the probability mass. Estimators of the form C‘.fi/are called minimum volume estimators. Observe that for Cbeing all Borel measurable sets, C.1/is the support of the density p corresponding to P, assuming it exists.(Note that C.1/is well defined even when p does not exist.) For smaller classes C, C.1/is the
https://dl.acm.org/citation.cfm?id=1119749
Suppose you are given some data set 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 equals some a priori specified value between 0 and 1. We propose a method to approach this problem by trying to estimate a function f that is positive on S and negative on ...Cited by: 4577
https://www.academia.edu/9321551/Estimating_the_Support_of_a_High-Dimensional_Distribution
Estimating the Support of a High-Dimensional Distribution
http://members.cbio.mines-paristech.fr/~jvert/svn/bibli/local/Scholkopf2001Estimating.pdf
Estimating the Support of a High-Dimensional Distribution 1445 of the probability mass. Estimators of the form C`(a)are called minimum volume estimators. Observe that forCbeing all Borel measurable sets,C(1)is the support of the densitypcorresponding toP, assuming it exists.(Note thatC(1)is well de” ned even whenpdoes not exist.)For smaller classesC,C(1)isthe
http://www.math.univ-toulouse.fr/~agarivie/Telecom/apprentissage/articles/OneClasslong.pdf
Estimating the Support of a High-Dimensional Distribution Bernhard Sch¨olkopf , John C. Platt , John Shawe-Taylor , Alex J. Smola , Robert C. Williamson Microsoft Research Ltd, 1 Guildhall Street, Cambridge CB2 3NH, UK Microsoft Research, 1 Microsoft Way, Redmond, WA, USA Royal Holloway, University of London, Egham, UK
https://www.mitpressjournals.org/doi/10.1162/089976601750264965
Mar 13, 2006 · Estimating the Support of a High-Dimensional Distribution Bernhard Schölkopf , John C. Platt , John Shawe-Taylor , Alex J. Smola and Robert C. Williamson Posted Online March 13, 2006Cited by: 4577
http://members.cbio.mines-paristech.fr/~jvert/svn/bibli/local/Scholkopf2001Estimating.pdf
Estimating the Support of a High-Dimensional Distribution 1445 of the probability mass. Estimators of the form C`(a)are called minimum volume estimators. Observe that forCbeing all Borel measurable sets,C(1)is the support of the densitypcorresponding toP, assuming it exists.(Note thatC(1)is well de” ned even whenpdoes not exist.)For smaller classesC,C(1)isthe
https://www.researchgate.net/publication/2907888_ARTICLE_Communicated_by_Vladimir_Vapnik_Estimating_the_Support_of_a_High-Dimensional_Distribution
Request PDF ARTICLE Communicated by Vladimir Vapnik Estimating the Support of a High-Dimensional Distribution This article describes an algorithm that …
http://core.ac.uk/display/23831033
Estimating the Support of a High-Dimensional Distribution . By ... Suppose you are given some data set 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 equals some a priori specified value between 0 and 1 ...
https://core.ac.uk/display/23586452
Estimating the Support of a High-Dimensional Distribution By Bernhard Schölkopf, John C. Platt, John Shawe-taylor, Alex J. Smola and Robert C. Williamson Abstract
https://www.deepdyve.com/lp/mit-press/estimating-the-support-of-a-high-dimensional-distribution-eE9PYO0qj4
Jul 01, 2001 · Read "Estimating the Support of a High-Dimensional Distribution, Neural Computation" on DeepDyve, the largest online rental service for scholarly research with thousands of academic publications available at your fingertips.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6453538/
This condition is strictly weaker than the condition under which the linear estimator attains a n-rate.Thus even in the n-situation higher order estimating equations may yield estimators that are applicable in a wider range of models. For instance, in the case that α = β the cut-off arises for α = β ≥ d/4, whereas reduces to α = β ≥ d/2.
https://projecteuclid.org/download/pdfview_1/euclid.aos/1359987530
A sparse precision matrix can be directly translated into a sparse Gaussian graphical model under the assumption that the data follow a joint normal distribution. This neat property makes high-dimensional precision matrix estimation very appealing in many applications.
https://arxiv.org/pdf/1707.09161.pdf
Empirical Bayes Estimators for High-Dimensional Sparse Vectors Pavan Srinath University of Cambridge [email protected] Ramji Venkataramanan University of Cambridge [email protected] December 31, 2018 Abstract The problem of estimating a high-dimensional sparse vector 2Rn from an observation in i.i.d. Gaussian noise is considered.
https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1541-0420.2010.01438.x
Regularized Sandwich Estimators for Analysis of High‐Dimensional Data Using Generalized Estimating Equations. David I. Warton. School of Mathematics and Statistics and Evolution and Ecology Research Centre, The University of New South Wales, NSW 2052, Australia ...
https://www.projecteuclid.org/download/pdfview_1/euclid.ejs/1455715952
This is an expository paper that reviews recent developments on optimal estimation of structured high-dimensional covariance and precision matrices. Minimax rates of convergence for estimating several classes of structured covariance and precision matrices, including bandable, Toeplitz, sparse, and sparse spiked covariance matrices as well as ...
https://arxiv.org/pdf/1707.00102.pdf
These data present a unique challenge because they are high-dimensional and observational. Our goal is to make personalized treatment recom-mendations based on the outcomes for past patients similar to a new patient. We propose and analyze three methods for estimating heteroge-neous treatment e ects using observational data. Our methods perform
http://theory.stanford.edu/~valiant/papers/VV_stoc11.pdf
Estimating the Unseen: An n/log(n)-sample Estimator for Entropy and Support Size, Shown Optimal via New CLTs∗ Gregory Valiant † UC Berkeley Berkeley, California [email protected] Paul Valiant ‡ UC Berkeley Berkeley, California [email protected] ABSTRACT We introduce a new approach to characterizing the unob-
http://www.kernel-machines.org/publications/SchPlaShaSmoetal99/bibliography_exportForm
News Call for NIPS 2008 Kernel Learning Workshop Submissions 2008-09-30 Tutorials uploaded 2008-05-13 Machine Learning Summer School / Course On The Analysis On Patterns 2007-02-12 New Kernel-Machines.org server 2007-01-30 Call for participation: The 2006 kernel workshop, "10 years of kernel machines" 2006-10-06
https://onlinelibrary.wiley.com/doi/pdf/10.1002/cjs.11526
Theoretical properties of the new estimator are established under a high‐dimensional setting where p grows with n almost exponentially fast. In particular, we show that the proposed estimator is consistent in both parameter estimation and support recovery.
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