Structural Modelling with Sparse Kernels

By Steve R. Gunn, Jaz S. Kandola. 2002

104 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

A low variance consistent test of relative dependency (2014, background): “For many problems in data analysis, however, the question of whether dependence exists is secondary: there may be multiple dependencies, and the question becomes which dependence is the strongest.”
Support Vector Machines with Applications (2006, unknown): “Remote Control 25 821–837. [2] AMARI, S.-I. (1985). Differential-Geometrical Methods in Statistics. Lecture Notes in Statist. 28. Springer, New York. MR0788689 [3] ARONSZAJN, N. (1950). Theory of reproducing kernels. Trans. Amer. Math. Soc. 68 337–404. MR0051437 [4] ARONSZAJN, N. (1951). Green’s fun”
Component selection and smoothing in smoothing spline analysis of variance models -- COSSO (2003, background): “Gunn and Kandola (2002) proposed a sparse kernel approach in a closely related framework.”
Least angle and ℓ1 penalized regression: A review (2008, methodology): “…kernel regression model, as well as a separate path-following algorithm for estimating the optimal kernel hyperparameter λ. Guigue et al. (2006) and Gunn and Kandola (2002) consider LASSO extensions to more flexible kernel regression models, in which each kernel function Kλ(xm, ·) is replaced by…”
Low-level interpretability and high-level interpretability: a unified view of data-driven interpretable fuzzy system modelling (2008, unknown): “…modelling methods, particularly for ameliorating the curse of dimensionality [75,97,163,164,177], a problem may happen in some cases for interpretable fuzzy modelling, i.e., they cannot provide a transparent solution if the object being modelled contains high-dimensional interactions [69].”
Nomograms for visualizing support vector machines (2005, background): “sparse and smooth kernels as a part of the optimization problem, combining the quest for decomposability and the actual learning [5, 20].”