[ABE-L] Fwd: Ciclo de Seminários do PPGEST-UnB

[Cibele]

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."--
-------------- Próxima Parte ----------
Um anexo em HTML foi limpo...
URL: <https://lists.ime.usp.br/archives/abe/attachments/20161129/dab2b15c/attachment.html>