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http://users.cecs.anu.edu.au/~williams/papers/P132.pdf
1448 BernhardScholkop¨ fetal. Notethatifºapproaches0,theupperboundariesontheLagrangemul- tiplierstendtoin”nity,thatis,thesecondinequalityconstraintinequa-tion3 ...
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.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
https://dl.acm.org/doi/10.1162/089976601750264965
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 ...
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 ...
http://scholar.google.com/citations?user=DZ-fHPgAAAAJ&hl=en
Estimating the support of a high-dimensional distribution. B Scholkopf, JC Platt, J Shawe-Taylor, AJ Smola, RC Williamson. 4509: 1999: Estimating the support of a high-dimensional distribution. B Schölkopf, JC Platt, J Shawe-Taylor, AJ Smola, RC Williamson ... MA Hearst, ST Dumais, E Osuna, J Platt, B Scholkopf. IEEE Intelligent Systems and ...
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://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
http://www.math.univ-toulouse.fr/~agarivie/Telecom/apprentissage/articles/OneClasslong.pdf
Estimating the support of a density. Observe that for being all Borel mea- surable sets, is the support of the density corresponding to , assuming it exists. (Note that is well defined even when does not exist.) For smaller classes , is the minimum volume containing the support of .
https://www.scirp.org/reference/referencespapers.aspx?referenceid=2641494
Scholkopf, B.S., Platt, J.C. and Shawe-Taylor, J.C. (2001) Estimating the Support of a High-Dimensional Distribution. Neural Computation, 13, 1443-1471.
http://www.recognition.mccme.ru/pub/papers/SVM/sch99estimating.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, UK
https://www.mitpressjournals.org/doi/abs/10.1162/089976601750264965
Mar 13, 2006 · Estimating the Support of a High-Dimensional Distribution. 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://cs.nyu.edu/~eugenew/ml/2008/weinstein-novel.pdf
Support Vector Novelty Detection Discussion of “Support Vector Method for Novelty Detection” (NIPS 2000) and “Estimating the Support of a High-Dimensional Distribution” (Neural Computation 13, 2001) Bernhard Scholkopf, John C. Platt, John Shawe-Taylor, Alex J. Smola, and Robert C. Williamson Eugene Weinstein March 3rd, 2009
https://core.ac.uk/display/45852707
Estimating the support of a high-dimensional distribution. By B. Schölkopf, ... 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 ...
https://eprints.soton.ac.uk/259007/
Estimating the Support of a High-Dimensional Distribution Estimating the Support of a High-Dimensional Distribution 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 ...
http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.39.912
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): 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.
https://www.mitpressjournals.org/doi/pdf/10.1162/NECO_a_00820
dom variables (one-dimensional or high-dimensional). Suppose we have two random variables (α,β), with characteristic functions fα and fβ,re-spectively.Theirjointcharacteristicfunctionis fα,β.Thedistancecovariance is defined as: Definition 1. ThedistancecovarianceC(α,β) between two random variables (α,β) is C2(α,β)= f α,β − fα β 2 (3.1)
https://www.quora.com/What-is-a-good-resource-for-understanding-One-Class-SVM-for-distribution-esitmation
Estimating the support of a high-dimensional distribution. Neural Computation, 13(7):1443{1471, 2001. [2] Yunqiang Chen, Xiang Zhou, and Thomas S. Huang, One-Class SVM For Learning in Image Retrieval, Proceedings of ICIP, 2001
https://www.cs.nmt.edu/~kdd/libsvm.pdf
2.3 Distribution Estimation (One-class SVM) One-class SVM was proposed by (Scholkopf et al., 2001) for estimating the support of a high-dimensional distribution.
http://www.pami.fudan.edu.cn/~jpzhang/literatures/Statistical%20Learning%20Theory/INDEX.HTM
SVM in Learning and Feature Extraction(Scholkopf) SV Learning(Scholkopf,slide) Estimating the Support of a High-Dimensional Distribution(Scholkopf) Generalization Performance of Regularization Networks and Support Vector Machines via Entropy Numbers of Compact Operators(Williamson,A.J.Smola,Scholkopf)
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