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By Alex Smola, Bernhard Schölkopf. 2000

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Citations in the corpus

On the Equivalence between Kernel Quadrature Rules and Random Feature Expansions (2015, methodology): “It has appeared under many names: Nystr¨om method (Williams and Seeger, 2001), sparse greedy approximations (Smola and Sch¨olkopf, 2000), incomplete Cholesky decomposition (Fine and Scheinberg, 2001), Gram-Schmidt orthonormalization (Shawe-Taylor and Cristianini, 2004) or CUR matrix de-compositions…”
Fastfood: Approximate Kernel Expansions in Loglinear Time (2014, background): “Low Rank Expansions Subsequent work by Smola and Sch¨olkopf (2000); Fine and Scheinberg (2001) and Williams and Seeger (2001) aimed to reduce memory footprint and complexity by ﬁnding subspaces to expand functions.”
A Kernel Method for the Two-Sample-Problem (2006, methodology): “We end by noting another potential approach to reducing the computational cost of the MMD, by computing a low rank approximation to the Gram matrix (Fine and Scheinberg, 2001; Williams and Seeger, 2001; Smola and Schölkopf, 2000).”
Scalable Kernel Methods via Doubly Stochastic Gradients (2014, neutral): “Many works, such as Greedy basis selection techniques [1], Nyström approximation [2] and incomplete Cholesky decomposition [3], all followed this strategy.”
Improved Nyström low-rank approximation and error analysis (2008, background): “In (Smola & Sch¨olkopf, 2000), the kernel matrix K is approximated by the subspace spanned by a subset of its columns.”
Fast and scalable polynomial kernels via explicit feature maps (2013, methodology): “In order to apply kernel methods to large-scale datasets, many approaches have been proposed for quickly approximating the kernel matrix, including the Nyström methods [5, 23], sparse greedy approximation [19] and low-rank kernel approximation [7].”
Core Vector Machines: Fast SVM Training on Very Large Data Sets (2005, methodology): “As shown in (Smola and Schölkopf, 2000), by using a small random sample of, say, size 59, the furthest point obtained fromS ′ is with probability 0.95 among the furthest 5% of points from the wholeS .”
Sparse On-Line Gaussian Processes (2002, background): “So far, a variety of sparsity techniques (Smola & Schölkopf, 2000; Williams & Seeger, 2001) for batch training of GPs have been proposed.”
Sparse Online Gaussian Processes (2008, background): “So far, a variety of sparsity techniques (Smola and Schölkopf 2000; Williams and Seeger 2001) for batch training of GPs have been proposed.”
Multicategory Proximal Support Vector Machine Classifiers (2005, background): “Other related reduction approaches are given in [14, 28, 29, 38].”
Predictive low-rank decomposition for kernel methods (2005, methodology): “…out routines that inspect all of the entries of K. Algorithms that meet this desideratum include the Nyström approximation (Williams and Seeger, 2000), sparse greedy approximations (Smola and Schölkopf, 2000) and incomplete Cholesky decomposition (Fine and Scheinberg, 2001, Bach and Jordan, 2002).”
Estimating a Kernel Fisher Discriminant in the Presence of Label Noise (2001, background): “Future work will focus on resolving these issues (see for example Smola & Sch¨olkopf, 2000).”
Fast approximate spectral clustering (2009, methodology): “These methods have been widely adopted, particularly in the context of approximations for the support vector machine (SVM) [9, 38, 10, 34].”
Construction of approximation spaces for reinforcement learning (2013, background): “However, finding a suitable subset is not trivial (Smola and Schölkopf, 2000; Csató and Opper, 2002).”