Hyperbolic M-SVM: a generalization of SVM

Date(s) : 19/03/2018   iCal
15 h 30 min - 16 h 30 min

In machine learning, support vector machines (SVMs) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. In this talk, I will introduce a new margin multi category classifier based on classes of vector valued functions with one component function per category, it is a kernel machine whose separation surfaces are hyperbolic and generalizes the SVMs. I will also exhibit the statistical properties of this classifier , I will show that the classes of component functions are uniform Glivenko-Cantelli (GC). I will then found the guaranteed risk of this classifier.
Finally, I will exhibit a margin loss function ensuring the Fisher consistency.


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