Learning Non-Linear Combinations of Kernels

By Corinna Cortes, Mehryar Mohri, Afshin Rostamizadeh. 2009

323 citations recorded by the source corpus.

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Citations in the corpus

Contrastive Multiview Coding (2019, background): “Closely related to self-supervised learning is the idea of multi-view learning, which is a general term involving many different approaches such as co-training [8], multi-kernel learning [13] and metric learning [6, 87]; for comprehensive surveys please see [80, 45].”
Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness (2020, background): “When deep classifiers make predictions on input examples that are far from the support of the training set, their performance can be arbitrarily bad [4, 14].”
A low variance consistent test of relative dependency (2014, background): “, 2013b), kernel regression tests (Cortes et al., 2009; Gunn & Kandola, 2002), rankings (Heller et al.”
A Survey on Multi-view Learning (2013, methodology): “Based on a combinatorial analysis of the Rademacher complexity of the hypothesis set under consideration, Cortes et al. (2010) presented another generalization bound with an L1 constraint that has only a logarithmic dependency on the kernel number k.”
Algorithms for Learning Kernels Based on Centered Alignment (2012, methodology): “…parameter of the SVM. Norm-2 regularized combination ( l2-krr ): this algorithm optimizes the kernel ridge regression objective The L 2 regularized method is used for regression since it is shown in (Cortes et al., 2009a) to outperform the alternative L 1 regularized method in similar settings.”
How to Scale Up Kernel Methods to Be As Good As Deep Neural Nets (2014, background): “However, empirically, we do not observe a strong diﬀerence and have adopted the second approach for its scalablility.”
A Binary Classification Framework for Two-Stage Multiple Kernel Learning (2012, methodology): “This one-stage approach was first proposed by (Lanckriet et al., 2004) and has since received significant attention (Rakotomamonjy et al., 2007; Sonnenburg et al., 2006; Cortes et al., 2010a; Kloft et al., 2011; Bach, 2008; Zien & Ong, 2007; Cortes et al., 2009; Sindhwani & Lozano, 2011).”
Multiple Kernel Learning Algorithms (2011, unknown): “Cortes et al. (2010) develop a nonlinear kernel combination method based on kernel ridge regression and polynomial combination of kernels.”
Generalized Latent Multi-View Subspace Clustering (2020, methodology): “As a result, the method in [32] directly combines multiple kernels corresponding to different views and validated the approach’s effectiveness.”
Kernel-Based Weighted Multi-view Clustering (2012, methodology): “Iterative frameworks are constantly gaining ground in multiple kernel learning [12], [23], [25], and are proving to be quite efﬁcient.”
Federated Multi-view Learning for Private Medical Data Integration and Analysis (2021, methodology): “These methods include linear combination methods [26, 32] and nonlinear combination methods [10].”
Simple and Efficient Multiple Kernel Learning by Group Lasso (2010, methodology): “A special case isL2-MKL (Kloft et al., 2008; Cortes et al., 2009a).”