Sparse Gaussian Process Classification With Multiple Classes

By Matthias W. Seeger, Michael I. Jordan. 2004

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Citations in the corpus

Kernels for Vector-Valued Functions: a Review (2011, methodology): “Alternatively, it is also possible to have parameters φ d associated to each output [110, 89].”
Bayesian Gaussian Process Classification with the EM-EP Algorithm (2006, methodology): “Recently, a sparse approximation method for multiclass classification in the traditional GPC formulation has been proposed in [28].”
A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation Propagation (2016, methodology): “…example, the multinomial probit likelihood can be handled in the same way as the binary case, where the log-normaliser of the tilted distribution can be computed using a C-dimensional Gaussian quadrature [C is the number of classes] (Seeger and Jordan, 2004) or nested EP (Riihimäki et al., 2013).”
A Unifying Framework for Sparse Gaussian Process Approximation using Power Expectation Propagation (2016, methodology): “Unifying Gaussian Process Approximation log-normaliser of the tilted distribution can be computed using a C-dimensional Gaussian quadrature [C is the number of classes] (Seeger and Jordan, 2004) or nested EP (Riihimäki et al.”
Variational Bayesian Multinomial Probit Regression with Gaussian Process Priors (2006, methodology): “…classification has been considered in Lawrence et al. (2003), Csato and Opper (2002), and Quinonero-Candela and Winther (2003), and it is clear from Seeger and Jordan (2004) that extension of ADF-based approximations such as IVM to the multiclass problem is not at all straightforward when a…”
Expectation Propagation as a Way of Life: A Framework for Bayesian Inference on Partitioned Data (2014, background): “Seeger and Jordan (2004) estimated the tilted moments in multiclass classification using multidimensional quadratures.”
Bayesian Online Multitask Learning of Gaussian Processes (2010, background): “This kind of problems (learning several tasks with the same kernel) also arises in multiclass classification [26] and functional data analysis [27], [28].”
Robust Multi-Class Gaussian Process Classification (2011, background): “Robust Multi-Class Gaussian Process Classification Consider n training instances in the form of a collection of feature vectors X = {x1, . . . , xn} with associated labels y = {y1, . . . , yn}, where yi ∈ C = {1, . . . , l}.”