[ABE-L] Seminarios em Aprendizado Estatistico de Maquinas

[Ronaldo Dias]

Prezados(as):

Gostaria de convidá-los a participar uma sequência de Seminários em:
*Kernel-based learning methods: *Applications toward Inference for Large
Scale Text Classification​.

Horário: quartas-feiras às 13 as 14hs.
Local: Sala 221 IMECC.

Interessados: RSVP a dias em ime.unicamp.br
Espaço limitado a 15 participantes.
Após o primeiro mês, é esperado de cada participante apresentar um
seminário sobre assunto.

Dentro vários, cito:

Parte do Abstract do livro: *I**nductive Inference for*

*Large Scale Text Classification. *Catarina Silva e Bernadete Ribeiro


Text classification is becoming a crucial task to analysts in different
areas. In the last few decades the production of textual documents in
digital form has increased exponentially. Their applications range from web
pages to scientific documents, including emails, news and books. Searching
for a digital text in Google is now more than a reality, it is a
commonplace. In the near future, with the advent of intelligent text
classification methods, people will have even more access to a large
variety of enhanced digital text services, viz. filtering, searching and
filing.

Despite the widespread use of digital texts, handling them is inherently
difficult - the large amount of data necessary to represent them and the
subjectivity of classification complicate matters. Earlier research has
addressed the extraction of information from relatively small collections
of well-structured documents such as news wires and scientific publications.

Intelligent text classification methods, which rely heavily on machine
learning algorithms, have the potential to supersede existing information
retrieval techniques and provide superior facilities that will save time
and money for users and companies, while providing a vital tool for dealing
with the proliferation of digital texts they are faced with.

​Extraido de: *Learning with Kernels: *
*Support Vector Machines, Regularization, Optimization, and Beyond​.​*

Bernhard Scholkopf
​ ​
Alexander J. Smola


 One of the most fortunate situations a scientist can encounter is to

enter a field in
​​
its infancy. There is a large choice of topics to work on, and many of the
issues
​ ​
are conceptual rather than merely technical.
​...​
​ ​

 The scope of the field has now widened significantly, both
​ ​
in terms of new algorithms, such as kernel methods different to SVMs, and in
​ ​
terms of a deeper theoretical understanding being gained. It has become
clear
​ ​
that kernel methods provide a framework for tackling some rather profound
​ ​
issues in machine learning theory. At the same time, successful
applications have
​ ​
demonstrated that SVMs not only have a more solid foundation than artificial
​ ​
neural networks, but are able to serve as a replacement for neural networks
that
​ ​
perform as well or better, in a wide variety of fields. Standard neural
network an
​ ​
pattern recognition textbooks have now started including chapters on SVMs
​and ​
​
kernel PCA​

​.​


-- 
Ronaldo Dias
Professor
Dept. of Statistics-IMECC, UNICAMP
www.ime.unicamp.br/~dias
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