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Large scale multiple kernel learning

by Sören Sonnenburg, Gunnar Rätsch , Christin Schäfer, Bernhard Schölkopf - JOURNAL OF MACHINE LEARNING RESEARCH , 2006
"... While classical kernel-based learning algorithms are based on a single kernel, in practice it is often desirable to use multiple kernels. Lanckriet et al. (2004) considered conic combinations of kernel matrices for classification, leading to a convex quadratically constrained quadratic program. We s ..."
Abstract - Cited by 340 (20 self) - Add to MetaCart
While classical kernel-based learning algorithms are based on a single kernel, in practice it is often desirable to use multiple kernels. Lanckriet et al. (2004) considered conic combinations of kernel matrices for classification, leading to a convex quadratically constrained quadratic program. We

Multiple kernel learning, conic duality, and the SMO algorithm

by Francis R. Bach, Gert R. G. Lanckriet - In Proceedings of the 21st International Conference on Machine Learning (ICML , 2004
"... While classical kernel-based classifiers are based on a single kernel, in practice it is often desirable to base classifiers on combinations of multiple kernels. Lanckriet et al. (2004) considered conic combinations of kernel matrices for the support vector machine (SVM), and showed that the optimiz ..."
Abstract - Cited by 445 (31 self) - Add to MetaCart
While classical kernel-based classifiers are based on a single kernel, in practice it is often desirable to base classifiers on combinations of multiple kernels. Lanckriet et al. (2004) considered conic combinations of kernel matrices for the support vector machine (SVM), and showed

Multiple kernel learning algorithms

by Mehmet Gönen, Ethem Alpaydın, Francis Bach - JMLR , 2011
"... In recent years, several methods have been proposed to combine multiple kernels instead of using a single one. These different kernels may correspond to using different notions of similarity or may be using information coming from multiple sources (different representations or different feature subs ..."
Abstract - Cited by 122 (2 self) - Add to MetaCart
subsets). In trying to organize and highlight the similarities and differences between them, we give a taxonomy of and review several multiple kernel learning algorithms. We perform experiments on real data sets for better illustration and comparison of existing algorithms. We see that though there may

Consistency of the group lasso and multiple kernel learning

by Francis R. Bach - JOURNAL OF MACHINE LEARNING RESEARCH , 2007
"... We consider the least-square regression problem with regularization by a block 1-norm, i.e., a sum of Euclidean norms over spaces of dimensions larger than one. This problem, referred to as the group Lasso, extends the usual regularization by the 1-norm where all spaces have dimension one, where it ..."
Abstract - Cited by 274 (33 self) - Add to MetaCart
are replaced by functions and reproducing kernel Hilbert norms, the problem is usually referred to as multiple kernel learning and is commonly used for learning from heterogeneous data sources and for non linear variable selection. Using tools from functional analysis, and in particular covariance operators

Localized Multiple Kernel Learning

by Mehmet Gönen, Ethem Alpaydın
"... Recently, instead of selecting a single kernel, multiple kernel learning (MKL) has been proposed which uses a convex combination of kernels, where the weight of each kernel is optimized during training. However, MKL assigns the same weight to a kernel over the whole input space. In this paper, we de ..."
Abstract - Cited by 55 (9 self) - Add to MetaCart
Recently, instead of selecting a single kernel, multiple kernel learning (MKL) has been proposed which uses a convex combination of kernels, where the weight of each kernel is optimized during training. However, MKL assigns the same weight to a kernel over the whole input space. In this paper, we

Multiclass multiple kernel learning

by Alexander Zien, Cheng Soon Ong - In ICML. ACM
"... In many applications it is desirable to learn from several kernels. “Multiple kernel learning” (MKL) allows the practitioner to optimize over linear combinations of kernels. By enforcing sparse coefficients, it also generalizes feature selection to kernel selection. We propose MKL for joint feature ..."
Abstract - Cited by 62 (4 self) - Add to MetaCart
In many applications it is desirable to learn from several kernels. “Multiple kernel learning” (MKL) allows the practitioner to optimize over linear combinations of kernels. By enforcing sparse coefficients, it also generalizes feature selection to kernel selection. We propose MKL for joint feature

Active Kernel Learning

by Steven C. H. Hoi, Rong Jin
"... Identifying the appropriate kernel function/matrix for a given dataset is essential to all kernel-based learning techniques. A number of kernel learning algorithms have been proposed to learn kernel functions or matrices from side information (e.g., either labeled examples or pairwise constraints). ..."
Abstract - Cited by 8 (2 self) - Add to MetaCart
Identifying the appropriate kernel function/matrix for a given dataset is essential to all kernel-based learning techniques. A number of kernel learning algorithms have been proposed to learn kernel functions or matrices from side information (e.g., either labeled examples or pairwise constraints

Online Learning with Kernels

by Jyrki Kivinen, Alexander J. Smola, Robert C. Williamson , 2003
"... Kernel based algorithms such as support vector machines have achieved considerable success in various problems in the batch setting where all of the training data is available in advance. Support vector machines combine the so-called kernel trick with the large margin idea. There has been little u ..."
Abstract - Cited by 2831 (123 self) - Add to MetaCart
use of these methods in an online setting suitable for real-time applications. In this paper we consider online learning in a Reproducing Kernel Hilbert Space. By considering classical stochastic gradient descent within a feature space, and the use of some straightforward tricks, we develop simple

Composite kernel learning

by Marie Szafranski, Yves Grandvalet,, Alain Rakotomamonjy - IN PROC. ICML , 2008
"... The Support Vector Machine (SVM) is an acknowledged powerful tool for building classifiers, but it lacks flexibility, in the sense that the kernel is chosen prior to learning. Multiple Kernel Learning (MKL) enables to learn the kernel, from an ensemble of basis kernels, whose combination is optimize ..."
Abstract - Cited by 32 (5 self) - Add to MetaCart
The Support Vector Machine (SVM) is an acknowledged powerful tool for building classifiers, but it lacks flexibility, in the sense that the kernel is chosen prior to learning. Multiple Kernel Learning (MKL) enables to learn the kernel, from an ensemble of basis kernels, whose combination

Wavelet Kernel Learning

by F. Yger, A. Rakotomamonjy
"... This paperaddressesthe problem ofoptimal featureextractionfrom a waveletrepresentation. Our workaims at building features by selecting waveletcoefficients resulting from signalorimage decomposition on an adapted wavelet basis. For this purpose, we jointly learn in a kernelized large-margin context t ..."
Abstract - Cited by 4 (2 self) - Add to MetaCart
This paperaddressesthe problem ofoptimal featureextractionfrom a waveletrepresentation. Our workaims at building features by selecting waveletcoefficients resulting from signalorimage decomposition on an adapted wavelet basis. For this purpose, we jointly learn in a kernelized large-margin context
Results 1 - 10 of 4,790