<html><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8" /></head><body style='font-size: 10pt; font-family: Verdana,Geneva,sans-serif'> Senhores, Encaminho a divulgação de duas palestras, nesta quinta, no Ciclo de Seminários do PPGEST-UnB. Os palestrantes são Gabriela Guimarães Olinto e André Lobato Ramos, egressos do Bacharelado em Estatística da UnB e Egressos do Rochester Institute of Technology/Center for Quality and Applied Statistics, ambos orientados pelo prof. Ernest Fokoué. Abraços, Cibele 1) “Kernelized Cost-Sensitive Listwise Ranking” Palestrante: Gabriela Guimarães Olinto (Data Scientist - Soleo Communications) M.Sc. School of Mathematical Sciences College of Science Rochester Institute of Technology. DATA: 01/12/2016 (quinta-feira) HORÁRIO: 14:30h LOCAL: Sala Multiuso EST Resumo “Learning to Rank is an area of application in machine learning, typically supervised, to build ranking models for Information Retrieval systems. The training data consists of lists of items with some partial order specified induced by an ordinal score or a binary judgment (relevant/not relevant). The model purpose is to produce a permutation of the items in this list in a way which is close to the rankings in the training data. This technique has been successfully applied to ranking, and several approaches have been proposed since then, including the listwise approach. A cost-sensitive version of that is an adaptation of this framework which treats the documents within a list with different probabilities, i.e. attempt to impose weights for the documents with higher cost. We then take this algorithm to the next level by kernelizing the loss and exploring the optimization in different spaces. We will show how the Kernel Cost-Sensitive ListMLE performs compared to the baseline Plain Cost-Sensitive ListMLE, ListNet, and RankSVM and show different aspects of the proposed loss function within different families of kernels. 2) “Evolutionary Weights for Random Subspace Learning” Palestrante: André Lobato Ramos. (Analista de marketing - Par Corretora) M.Sc. School of Mathematical Sciences College of Science Rochester Institute of Technology. DATA: 01/12/2016 (quinta-feira) HORÁRIO: 15:15h LOCAL: Sala Multiuso EST Resumo: “Ensemble learning is a widely used technique in Data Mining, this method allows us to aggregate models to reduce prediction error. There are many methods on how to perform model aggregation, one of them is known as Random Subspace Learning, which consists of building subspace of the feature space where we want to create our models. The task of selecting good subspaces and in turn produce good models for better prediction can be a daunting one, so we want to propose a new method to accomplish such a task. This proposed method allows for an automated data-driven way to attribute weights to variables in the feature space in order select variables that show themselves to be important in reducing the prediction error.”-- </body></html>