Multiple kernel learning

Known as: MKL 
Multiple kernel learning refers to a set of machine learning methods that use a predefined set of kernels and learn an optimal linear or non-linear… (More)
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Topic mentions per year

Topic mentions per year

2004-2018
05010020042018

Papers overview

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Highly Cited
2012
Highly Cited
2012
Cross-domain learning methods have shown promising results by leveraging labeled patterns from the auxiliary domain to learn a… (More)
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Highly Cited
2011
Highly Cited
2011
Learning linear combinations of multiple kernels is an appealing strategy when the right choice of features is unknown. Previous… (More)
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Highly Cited
2011
Highly Cited
2011
In solving complex visual learning tasks, adopting multiple descriptors to more precisely characterize the data has been a… (More)
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Highly Cited
2009
Highly Cited
2009
Recent advances in Multiple Kernel Learning (MKL) have positioned it as an attractive tool for tackling many supervised learning… (More)
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Highly Cited
2008
Highly Cited
2008
Recently, instead of selecting a single kernel, multiple kernel learning (MKL) has been proposed which uses a convex combination… (More)
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Highly Cited
2008
Highly Cited
2008
Learning linear combinations of multiple kernels is an appealing strategy when the right choice of features is unknown. Previous… (More)
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Highly Cited
2007
Highly Cited
2007
In many applications it is desirable to learn from several kernels. "Multiple kernel learning" (MKL) allows the practitioner to… (More)
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Highly Cited
2007
Highly Cited
2007
An efficient and general multiple kernel learning (MKL) algorithm has been recently proposed by Sonnenburg et al. (2006). This… (More)
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Highly Cited
2006
Highly Cited
2006
While classical kernel-based learning algorithms are based on a single kernel, in practice it is often desirable to use multiple… (More)
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Highly Cited
2004
Highly Cited
2004
While classical kernel-based classifiers are based on a single kernel, in practice it is often desirable to base classifiers on… (More)
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