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Multidimensional classifiers for neuroanatomical data

Abstract : This study explores the benefits of using mul-tidimensional classification. Its novelty lies in the application of state-of-the-art machine learning techniques to the Neuromorpho dataset. We formulate a supervised classification problem for predicting specie, gender, level one cell type, level two cell type, development stage and area of the neocortex based of a set of morphological features extracted from a neuron.
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Submitted on : Monday, November 16, 2015 - 8:46:53 AM
Last modification on : Friday, November 20, 2015 - 10:31:16 AM
Long-term archiving on: : Friday, April 28, 2017 - 7:14:41 AM


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  • HAL Id : hal-01225249, version 1


Pablo Fernandez-Gonzalez, Concha Bielza, Pedro Larranaga. Multidimensional classifiers for neuroanatomical data. ICML Workshop on Statistics, Machine Learning and Neuroscience (Stamlins 2015), Bertrand Thirion, Lars Kai Hansen, Sanmi Koyejo, Jul 2015, Lille, France. ⟨hal-01225249⟩



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