Building Support Vector Machines with Reduced Classifier Complexity

By S. Sathiya Keerthi, Olivier Chapelle, Dennis DeCoste, Kristin P. Bennett, Emilio Parrado-Hernández. 2006

318 citations recorded by the source corpus.

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Citations in the corpus

Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels (2014, background): “There are also approaches that are more speciﬁc to the objective function at hand, e.g., Keerthi et al. [2006] build a kernel expansion greedily to optimize the SVM objective function.”
Efficient Classification for Additive Kernel SVMs (2013, background): “, l1-norm on the alphas) or picks support vectors in a greedy manner till a stopping criterion is met [30].”
Classification using intersection kernel support vector machines is efficient (2008, background): “L 1 -norm on the alphas) or pick support vectors in a greedy manner till a stopping criteria is met [14].”
Surface EMG in advanced hand prosthetics (2008, background): “…have been tried, e.g. in Orabona et al. (2007), in which sparseness is achieved without losing any accuracy, or, e.g. Lee and Mangasarian (2001); Keerthi et al. (2006) where a subset of the support vectors is heuristically selected and fewer support vectors are traded for larger…”
Online Kernel Principal Component Analysis: A Reduced-Order Model (2012, background): “Various criteria for quantifying the relevance of a kernel function in SVM are put forward by Keerthi et al. in the more recent work [43].”
Supervised classification and mathematical optimization (2013, background): “This may be useful if a reduced expression of ω in terms of a set of functions φ is sought, as in [131].”
Training and Testing Low-degree Polynomial Data Mappings via Linear SVM (2010, methodology): “They propose methods to manipulate the kernels (e.g., Lee and Mangasarian, 2001; Keerthi et al., 2006).”
Breaking the curse of kernelization: budgeted stochastic gradient descent for large-scale SVM training (2012, methodology): “A similar method to build reduced SVM classifier based on forward selection was proposed by Keerthi et al. (2006).”