Maria Kesa

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This paper exploits the embedding provided by the counting grid model and proposes a framework for the classification and the analysis of brain MRI images. Each brain, encoded by a count of local features, is mapped into a window on a grid of feature distributions. Similar sample are mapped in close proximity on the grid and their commonalities in their(More)
Hybrid generative-discriminative models are useful in biomedical applications– generative modeling extracts interpretable features from raw data, highlighting its properties and increasing classification accuracy when used as input for a discriminative classifier. This raises the question: which generative model should be used for a particular application?(More)
OBJECTIVE High-throughput technologies have generated an unprecedented amount of high-dimensional gene expression data. Algorithmic approaches could be extremely useful to distill information and derive compact interpretable representations of the statistical patterns present in the data. This paper proposes a mining approach to extract an informative(More)
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