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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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Related topics

Related topics

11 relations

Broader (1)

Data mining
Elastic net regularizationGibbs samplingKernel methodMATLAB

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2014
2014
On p-norm Path Following in Multiple Kernel Learning for Non-linear Feature Selection
Our objective is to develop formulations and algorithms for efficiently computing the feature selection path - i.e. the variation… 
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
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… 
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… 
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… 
2009
2009
Multiple Kernel Learning for Object Classification
: Combining information from various image descriptors has become a standard technique for image classification tasks. Multiple… 
Review
2009
Review
2009
Localized Multiple Kernel Machines for Image Recognition
We review our work on localized multiple kernels (Gonen and Alpaydn, 2008, 2009) that allows kernels to be combined with dierent… 
2008
2008
Learning subspace kernels for classification
Kernel methods have been applied successfully in many data mining tasks. Subspace kernel learning was recently proposed to… 
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