Column-generation boosting methods for mixture of kernels

By Jinbo Bi, Tong Zhang, Kristin P. Bennett. 2004

96 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Boolean Decision Rules via Column Generation (2018, methodology): “We note that CG has been proposed for other machine learning tasks such as boosting [21, 7] and hash learning [37].”
A Survey on Multi-view Learning (2013, methodology): “Bi et al. (2004) used the 2-norm regularization approach to extend LPBoost to a quadratic program (QP), so that many successful formulations, such as classic SVMs, ridge regression, etc, could benefit from CG techniques. Crammer et al. (2002) used the boosting paradigm to perform the kernel construc”
On feature combination for multiclass object classification (2009, unknown): “m=1Km(x) T c;m +bc] 2 R C F N (;b )c, ind. Cc [ 2 ] b 2 RC”
Large Scale Multiple Kernel Learning (2006, methodology): “This problem is very similar to Equation (9) in Bi et al. (2004) when " composite kernels, " i.e. linear combinations of kernels are considered.”
Coupled Kernel Embedding for Low-Resolution Face Image Recognition (2012, background): “Compared with highresolution (HR) images, these LR images lose some discriminative details across different subjects.”
More generality in efficient multiple kernel learning (2009, methodology): “We compare our formulation to traditional MKL as well as the following feature selection methods Boosting : The LPBoost formulation of (Bi et al., 2004) is similar to that of standard MKL and boosting generalizes standard MKL’s decision function.”
Building Support Vector Machines with Reduced Classifier Complexity (2006, methodology): “The method of Bi et al. (2004) uses column generation ideas from linear and quadratic programming to select new basis functions and so it requires the solution of, both, the primal and dual problems.”
Binarized Support Vector Machines (2010, methodology): “…initially introduced for the cutting-stock problem (Gilmore and Gomory 1961) and successfully used, under different variants, in different works on support vector machines such as Bi et al. (2004), Bradley and Mangasarian (2000), Demiriz et al. (2002), and Mangasarian and Thompson (2006).”
Multiple Kernel Learning Algorithms (2011, unknown): “Bi et al. (2004) develop column generation boosting methods for binary classification and regression estimation problems. In each iteration, the proposed methods solve an LP or a QP on a working set depending on the regularization term used. Crammer et al. (2003) modify the boosting methodology to w”