Learning Kernel Classifiers: Theory and Algorithms

By Ralf Herbrich. 2001

576 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/23d2d8b687d31b11573473a7c7792b7ec08d0745

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In Artificial Intelligence

Citations in the corpus

The cross entropy method for classification (2005, background): “First, sparse classifiers typically have better generalization error (e.g., Floyd & Warmuth, 1995 and more recently within the luckiness framework of Herbrich and Williamson (2002) and via compression bounds of Herbrich, 2002 ).”
Kernel methods in machine learning (2007, unknown): “In doing so, we build on various sources, including Burges [25], Cristianini and Shawe-Taylor [37], Herbrich [64] and Vapnik [141] and, in particular, Schölkopf and Smola [118], but we also add a fair amount of more recent material which helps unifying the exposition.”
Text Mining Infrastructure in R (2008, background): “String kernels (Lodhi et al. 2002; Shawe-Taylor and Cristianini 2004; Watkins 2000; Herbrich 2002) are defined as a similarity measure between two sequences of characters x and y.”
Sparse multinomial logistic regression: fast algorithms and generalization bounds (2005, background): “For example, in a kernel classifier, the basis functions will be kernels centered at the training samples, meaning that d will equal n regardless of the number of originally observed features [5], [15].”
Fast Kernels for String and Tree Matching (2002, methodology): “This is a significant improvement, since the so-far fastest methods [8, 3] rely on dynarrtic programming which incurs a quadratic cost in the length of the argument.”
Support Vector Machines with Applications (2006, methodology): “An exposition of kernel methods with a Bayesian taste can be read in the book by Herbrich [30].”
A discriminative framework for clustering via similarity functions (2008, background): “We then analyze a much weaker average-attraction property in Section 4 that has close connections to large margin properties studied in Learning Theory [6, 7, 21, 31, 29].”
A tutorial on support vector regression (2004, unknown): “Additionally, [Cristianini and Shawe-Taylor, 2000, Herbrich, 2002] providefurther details on kernels in the context of classi(cid:2)cation.”
Online learning with kernels (2001, background): “Assuming that some achieves for some , the kernelized version of the Perceptron Convergence Theorem [27], [29] gives Consider now the more general case where the sequence is not linearly separable in the feature space.”
Kernel-based fuzzy clustering and fuzzy clustering: A comparative experimental study (2010, background): “Given two kernel functions, K1(x, y) and K2(x, y), one can show that K1(x, y) + K2(x, y), K1(x, y)K2(x, y) and aK1(x, y) where a > 0 are also kernels [14].”
High-level event recognition in unconstrained videos (2012, background): “Kernel-based classifiers have been popular in a wide range of applications for many years [45].”