21 mai 2017

    Séminaire départemental - Mercredi 24 mai 2017 Maximum Margin Interval Trees

    Alexandre Drouin et Toby Dylan Hocking Heure: 13h00 Local: PLT-3775

    Résumé: Learning a regression function using censored or interval-valued output data is an important problem in fields such as genomics and medicine. The goal is to learn a real-valued prediction function, and the training output labels indicate an interval of possible values. Whereas most existing algorithms for this task are linear models, in this paper we investigate learning nonlinear tree models. We propose to learn a tree by minimizing a margin-based discriminative objective function, and we provide a dynamic programming algorithm for computing the optimal solution in log-linear time. We show empirically that this algorithm achieves state-of-the-art speed and prediction accuracy in a benchmark of several data sets.

    Note: Une partie de la présentation sera en français et une autre partie en anglais.