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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… 
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Broader (1)

Data mining
Elastic net regularizationGibbs samplingKernel methodMATLAB

Papers overview

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2014
2014
Beta Process Multiple Kernel Learning
In kernel based learning, the kernel trick transforms the original representation of a feature instance into a vector of… 
2013
2013
Multi-task Multiple Kernel Learning
We present a general regularization-based framework for Multi-task learning (MTL), in which the similarity between tasks can be… 
2012
2012
Evaluation and improvement of spectral features for the detection of buried explosive hazards using forward-looking ground-penetrating radar
We provide an evaluation of spectral features extracted from the signal return of a forward-looking ground penetrating radar to… 
2011
2011
Learning neighborhood cooccurrence statistics of sparse features for human activity recognition
A common approach to activity recognition has been the use of histogram of codewords computed from Spatio Temporal Interest… 
2011
2011
Learning Neighborhood Co-occurrence Statistics of Sparse Features for Human Activity Recognition
A common approach to activity recognition has been the use of histogram of codewords computed from Spatio Temporal Interest… 
2011
2011
Localized Multiple Kernel Learning for Realistic Human Action Recognition in Videos
Realistic human action recognition in videos has been a useful yet challenging task. Video shots of same actions may present huge… 
2010
2010
Multiple Kernel Learning with High Order Kernels
Previous Multiple Kernel Learning approaches (MKL) employ different kernels by their linear combination. Though some improvements… 
2009
2009
Bayesian Localized Multiple Kernel Learning
Multiple kernel learning approaches to multi-view learning [1, 11, 7] have recently become very popular since they can easily… 
2008
2008
Multiple kernel learning for speaker verification
Many speaker verification (SV) systems combine multiple classifiers using score-fusion to improve system performance. For SVM… 
2008
2008
Multiple kernel learning from sets of partially matching image features
Recent publications and developments based on SVM have shown that using multiple kernels instead of a single one can enhance… 
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