Smoothing spline ANOVA models for large data sets with Bernoulli observations and the randomized GACV

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Robust Truncated Hinge Loss Support Vector Machines (2007, methodology): “…use convex losses as surrogates of the 0–1 loss, for example the hinge loss of the SVM, the logistic loss in the penalized logistic regression (Lin et al. 2000; Zhu and Hastie 2005), and the exponential loss function used in AdaBoost (Friedman, Hastie and Tibshirani 2000 Despite its wide…”
Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets (2014, methodology): “, binary or count) data is also straightforward using spatial generalized linear models (GLM’s) (Diggle et al. 1998; Lin et al. 2000; Kammann and Wand 2003; Banerjee et al. 2014).”
Component selection and smoothing in smoothing spline analysis of variance models -- COSSO (2003, methodology): “used by Xiang and Wahba (1996), Ruppert and Carroll (2000), Lin, Wahba, Xiang, Gao, Klein and Klein (2000) and Yau, Kohn and Wood (2002).”
Kernel Logistic Regression and the Import Vector Machine (2001, unknown): “(B2) For eachxl 2 R, let fl(x) = X xj2S[fxlg ajK(x; xj) Find~a to minimize H(xl) = N Xi=1 [yifl(xi) ln(1 + exp(fl(xi)))℄ + 2 kfl(x)k2HK = ~yT (Kl a~ al) +~1T ln(1 + exp(Kl a~ al)) + 2 ~ alTKl q ~ al (6) where the regressor matrix Kl a = [K(xi; xj)℄N (q+1), xi 2 fx1; x2; : : : xNg, xj 2 S [ fxlg; the”