首页|Reports Outline Machine Learning Study Results from University of Rouen Normandie (Random Forest Kernel for High-dimension Low Sample Size Classification)
Reports Outline Machine Learning Study Results from University of Rouen Normandie (Random Forest Kernel for High-dimension Low Sample Size Classification)
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Springer Nature
Data detailed on Machine Learning have been presented. According to news reporting originating in Rouen, France, by NewsRx journalists, research stated, “High dimension, low sample size (HDLSS) problems are numerous among real-world applications of machine learning. From medical images to text processing, traditional machine learning algorithms are usually unsuccessful in learning the best possible concept from such data.” Financial supporters for this research include This work is part of the DAISI project, co-financed by the European Union with the European Regional Development Fund (ERDF) and by the Normandy Region., DAISI project - European Union, European Union (EU), Normandy Region. The news reporters obtained a quote from the research from the University of Rouen Normandie, “In a previous work, we proposed a dissimilarity-based approach for multi-view classification, the random forest dissimilarity, that perfoms state-of-the-art results for such problems. In this work, we transpose the core principle of this approach to solving HDLSS classification problems, by using the RF similarity measure as a learned precomputed SVM kernel (RFSVM). We show that such a learned similarity measure is particularly suited and accurate for this classification context.”
RouenFranceEuropeCyborgsEmerging TechnologiesMachine LearningUniversity of Rouen Normandie
NETL
NSTL
Springer Nature
