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We consider the problem of learning a set from random samples.
Functions of positive and negative type and their connection with the theory of integral equations
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A Probabilistic Theory of Pattern Recognition
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A plug-in approach to support estimation
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On nonparametric estimation of density level sets
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Nonlinear component analysis as a kernel eigenvalue problem
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Correction: optimum bounds for the distributions of martingales in Banach spaces
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The Elements of Statistical Learning
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Estimating the support of a high-dimensional distribution
B. Schölkopf, J. Platt, J. Shawe-Taylor, A. Smola, and R. Williamson · 2001
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On the mathematical foundations of learning
F. Cucker and S. Smale · 2002
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Real Analysis and Probability
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A distribution-free theory of nonparametric regression
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On the influence of the kernel on the consistency of support vector machines
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On the convergence of eigenspaces in kernel principal component analysis,
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On regularization algorithms in learning theory
F. Bauer, S. Pereverzev, and L. Rosasco · 2007
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Optimal rates for the regularized least-squares algorithm
A. Caponnetto and E. De Vito · 2007
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Kernel PCA for novelty detection
H. Hoffmann · 2007
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Notes on regularized least squares
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Towards a theoretical foundation for Laplacian-based manifold methods
M. Belkin and P. Niyogi · 2008
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Laplacian eigenmaps for dimensionality reduction and data representation
M. Belkin and P. Niyogi · 2003
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Double operator integrals in a Hilbert space
M. Birman and M. Solomyak · 2003
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Set estimation: an overview and some recent developments,
A. Cuevas and A. Rodríguez-Casal · 2003
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Mesh-independent surface interpolation,
D. Levin · 2003
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The mathematics of learning: dealing with data
T. Poggio and S. Smale · 2003
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Random polytopes and the Efron-Stein jackknife inequality
M. Reitzner · 2003
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Spectral algorithms for supervised learning
L. Lo Gerfo, L. Rosasco, F. Odone, E. De Vito, and A. Verri · 2008
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Support vector machines
I. Steinwart and A. Christmann · 2008
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Asymptotic normality in density support estimation
G. Biau, B. Cadre, D. Mason, and B. Pelletier · 2009
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Anomaly detection: A survey
V. Chandola, A. Banerjee, and V. Kumar · 2009
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Elastic-net regularization in learning theory
C. De Mol, E. De Vito, and L. Rosasco · 2009
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Geometry of probability spaces
S. Smale and D. X. Zhou · 2009
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Cross-validation based adaptation for regularization operators in learning theory
A. Caponnetto and Y. Yao · 2010
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Vector valued reproducing kernel hilbert spaces and universality
C. Carmeli, E. De Vito, A. Toigo, and V. Umanità · 2010
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Set estimation,
A. Cuevas and R. Fraiman · 2010
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Spectral regularization for support estimation,
E. De Vito, L. Rosasco, and A. Toigo · 2010
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Probabilistic recovery of multiple subspaces in point clouds by geometric ℓ p \ell_{p} minimization
G. Lerman and T. Zhang · 2010
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K K -dimensional coding schemes in hilbert spaces
A. Maurer and M. Pontil · 2010
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Sample complexity of testing the manifold hypothesis,
H. Narayanan and S. Mitter · 2010
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On learning with integral operators
L. Rosasco, M. Belkin, and E. De Vito · 2010
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Some recent advances in multiscale geometric analysis of point clouds,
G. Chen, A. Little, M. Maggioni, and L. Rosasco · 2011
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Oracle inequalities in empirical risk minimization and sparse recovery problems
V. Koltchinskii · 2011
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A topological view of unsupervised learning from noisy data
P. Niyogi, S. Smale, and S. Weinberger · 2011
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Multiscale Geometric Methods for Data Sets II: Geometric Multi-Resolution Analysis
W. K. Allard, G. Chen, and M. Maggioni · 2012
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Mercer’s theorem on general domains: on the interaction between measures, kernels, and RKHSs
I. Steinwart and C. Scovel · 2012
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