Group-based local adaptive deep multiple kernel learning with lp norm [PDF]
PLoS ONE, 2020 The deep multiple kernel Learning (DMKL) method has attracted wide attention due to its better classification performance than shallow multiple kernel learning.
Shengbing Ren +5 more
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Multimodal Early Birth Weight Prediction Using Multiple Kernel Learning [PDF]
Sensors, 2023 In this work, a novel multimodal learning approach for early prediction of birth weight is presented. Fetal weight is one of the most relevant indicators in the assessment of fetal health status.
Lisbeth Camargo-Marín +3 more
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Characterizing social and cognitive EEG-ERP through multiple kernel learning [PDF]
Heliyon, 2023 EEG-ERP social-cognitive studies with healthy populations commonly fail to provide significant evidence due to low-quality data and the inherent similarity between groups.
Daniel Nieto Mora +4 more
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An Oil Painters Recognition Method Based on Cluster Multiple Kernel Learning Algorithm [PDF]
IEEE Access, 2019 A lot of image processing research works focus on natural images, such as in classification, clustering, and the research on the recognition of artworks (such as oil paintings), from feature extraction to classifier design, is relatively few.
Zhifang Liao +5 more
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Supervised multiple kernel learning approaches for multi-omics data integration [PDF]
BioData MiningBackground Advances in high-throughput technologies have originated an ever-increasing availability of omics datasets. The integration of multiple heterogeneous data sources is currently an issue for biology and bioinformatics.
Mitja Briscik +4 more
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Heterogeneous multiple kernel learning for breast cancer outcome evaluation [PDF]
BMC Bioinformatics, 2020 Background Breast cancer is one of the common kinds of cancer among women, and it ranks second among all cancers in terms of incidence, after lung cancer. Therefore, it is of great necessity to study the detection methods of breast cancer.
Xingheng Yu, Xinqi Gong, Hao Jiang
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PrognosiT: Pathway/gene set-based tumour volume prediction using multiple kernel learning [PDF]
BMC Bioinformatics, 2021 Background Identification of molecular mechanisms that determine tumour progression in cancer patients is a prerequisite for developing new disease treatment guidelines.
Ayyüce Begüm Bektaş, Mehmet Gönen
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Multiple-kernel learning for genomic data mining and prediction [PDF]
BMC Bioinformatics, 2019 Background Advances in medical technology have allowed for customized prognosis, diagnosis, and treatment regimens that utilize multiple heterogeneous data sources.
Christopher M. Wilson +4 more
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Automatic plankton image classification combining multiple view features via multiple kernel learning [PDF]
BMC Bioinformatics, 2017 Background Plankton, including phytoplankton and zooplankton, are the main source of food for organisms in the ocean and form the base of marine food chain.
Haiyong Zheng +5 more
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Kernel Matrix-Based Heuristic Multiple Kernel Learning
Mathematics, 2022 Kernel theory is a demonstrated tool that has made its way into nearly all areas of machine learning. However, a serious limitation of kernel methods is knowing which kernel is needed in practice.
Stanton R. Price +3 more
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