Results 241 to 250 of about 355,362 (266)

A primal method for multiple kernel learning

Neural Computing and Applications, 2012
The canonical support vector machines (SVMs) are based on a single kernel, recent publications have shown that using multiple kernels instead of a single one can enhance interpretability of the decision function and promote classification accuracy. However, most of existing approaches mainly reformulate the multiple kernel learning as a saddle point ...
Zhifeng Hao, Ganzhao Yuan, Xiaowei Yang 0003, Zijie Chen   +3 more
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Multiple Kernel Learning for Drug Discovery

Molecular Informatics, 2012
AbstractThe support vector machine (SVM) methodology has become a popular and well‐used component of present chemometric analysis. We assess a relatively recent development of the algorithm, multiple kernel learning (MKL), on published structure‐property relationship (SPR) data.
Nicholas C V, Pilkington, Matthew W B, Trotter, Sean B, Holden   +2 more
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Structural multiple empirical kernel learning

Information Sciences, 2015
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zhe Wang 0002, Qi Fan, Sheng Ke, Daqi Gao   +3 more
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Greedy Unsupervised Multiple Kernel Learning

2012
Multiple kernel learning (MKL) has emerged as a powerful tool for considering multiple kernels when the appropriate representation of the data is unknown. Some of these kernels may be complementary, while others irrelevant to the learning task. In this work we present an MKL method for clustering.
Grigorios Tzortzis, Aristidis Likas
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Extreme learning machine with multiple kernels

2013 10th IEEE International Conference on Control and Automation (ICCA), 2013
Recently a novel learning algorithm called extreme learning machine (ELM) was proposed for efficiently training single-hidden layer feedforward neural networks (SLFNs). Compared with other traditional gradient-descent-based learning algorithms, ELM has shown promising results because it chooses weights and biases of hidden nodes randomly and obtains ...
Li-juan Su, Min Yao
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Multiple Kernel Learning with Many Kernels

2013
Multiple kernel learning (MKL) addresses the problem of learning the kernel function from data. Since a kernel function is associated with an underlying feature space, MKL can be considered as a systematic approach to feature selection. Many of the existing MKL algorithms perform kernel learning by combining a given set of base kernels. While efficient
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Multiple Kernel Learning Improved by MMD

2010
When training and testing data are drawn from different distributions, the performance of the classification model will be low. Such a problem usually comes from sample selection bias or transfer learning scenarios. In this paper, we propose a novel multiple kernel learning framework improved by Maximum Mean Discrepancy (MMD) to solve the problem. This
Jiangtao Ren, Zhou Liang, Shaofeng Hu
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Veto-Consensus Multiple Kernel Learning

Proceedings of the AAAI Conference on Artificial Intelligence, 2016
We propose Veto-Consensus Multiple Kernel Learning (VCMKL), a novel way of combining multiple kernels such that one class of samples is described by the logical intersection (consensus) of base kernelized decision rules, whereas the other classes by the union (veto) of their complements.
Zhou, Y., Hu, N., Spanos, C.J.
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Large Scale Multiple Kernel Learning.

J. Mach. Learn. Res., 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 show that it can be rewritten as a semi-infinite linear
Sonnenburg, S., Rätsch, G., Schäfer, C., Schölkopf, B.   +3 more
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lp-Norm Multiple Kernel Learning

2011
Ziel des Maschinellen Lernens ist das Erlernen unbekannter Konzepte aus Daten. In vielen aktuellen Anwendungsbereichen des Maschinellen Lernens, wie zum Beispiel der Bioinformatik oder der Computer Vision, sind die Daten auf vielfältige Art und Weise in Merkmalsgruppierungen repräsentiert.
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