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Everything about Multiple kernel learning
Set of machine learning methods
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 combination of kernels as part of the algorithm. Reasons to use multiple kernel learning include a) the ability to select for an optimal kernel and parameters from a larger set of kernels, reducing bias due to kernel selection while allowing for more automated machine learning methods, and b) combining data from different sources that have different notions of similarity and thus require different kernels. Instead of creating a new kernel, multiple kernel algorithms can be used to combine kernels already established for each individual data source. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Multiple kernel learningA 5-minute orientation
- Skim a recent paper: Active Fire Detection from Landsat-8 Imagery Using Deep Multiple Kernel LearningInfluential work since 2022
- Pick up a classic book: Computational Linguistics and Intelligent Text ProcessingThe most-reprinted book on the subject
- Read the landmark paper: Domain Transfer Multiple Kernel LearningCited 433 times
- Go sideways: Support vector machineA closely related topic
- Go sideways: Kernel methodA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Domain Transfer Multiple Kernel Learning
Multiple Kernel Learning for Dimensionality Reduction
Multiple kernel extreme learning machine
EasyMKL: a scalable multiple kernel learning algorithm
Discriminative Multiple Kernel Learning for Hyperspectral Image Classification
Soft Margin Multiple Kernel Learning
Sparsity in multiple kernel learning
Localized algorithms for multiple kernel learning
🆕 Influential since 2022
Active Fire Detection from Landsat-8 Imagery Using Deep Multiple Kernel Learning
Incomplete multiview subspace clustering based on multiple kernel low-redundant representation learning
Ensembled Transfer Learning and Multiple Kernel Learning for Phonocardiogram Based Atherosclerotic Coronary Artery Disease Detection
Supervised multiple kernel learning approaches for multi-omics data integration
Predicting miRNA–Disease Associations Through Deep Autoencoder With Multiple Kernel Learning
📚 Books worth reading
Computational Linguistics and Intelligent Text ProcessingAlexander Gelbukh · 2003
Linear Algebra with ApplicationsGareth Williams · 1984
The Elements of Statistical LearningTrevor Hastie, Robert Tibshirani · 2001
Gaussian processes for machine learningCarl Edward Rasmussen · 2005
Information Processing in Medical ImagingJames C. Gee · 2013
Biometric RecognitionWei-Shi Zheng · 2012
Advanced Machine Learning Technologies and ApplicationsAboul Ella Hassanien · 2012
IPython Interactive Computing and Visualization CookbookCyrille Rossant · 2014
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
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